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31-08-2012, Amsterdam

RE ThesisContinuous control monitoring in electricity trading

Authors: Tom Wondergem MSc 1505343Sander Landzaat MSc 1346423

Mentor: Paul Harmzen RE RA

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Management summary

The main objective of this thesis is to determine whether a best practice framework for internal (continuous)control monitoring for electricity trading can be defined? Based on a literature study, we define 7 main riskcategories for electricity trading, namely: Credit Risk (High inherent risk), Market Risk (High inherent risk),Operational Risk (High inherent risk), Physical Delivery Risk (High inherent risk), Regulatory Risk (Mediuminherent risk) and Legal (Contract) Risk (Medium inherent risks). Based on a case study at an electricity tradingcompany, expert interviews and validation with line management, risk management and internal audit aframework of 42 controls is constructed to manage electricity trading risk by means of continuous controlmonitoring.

Of the developed framework, most added value is perceived in continuous automated pre-deal calculation ofValue at Risk (VaR) and Liquidity at Risk (LaR). In addition, continuous automated forecasting and continuousautomated back testing of forecasting models is perceived as having added value.

During discussion of the framework, we noted several limitations in the electricity trading market andmanagement risk appetite that may impact the possibility of implementing continuous control monitoring forelectricity trading. First, the current level of maturity of the IT Environment (including Energy Trading andRisk Management platforms) and IT General Controls may not fully support more complex continuous controlmonitoring such as pre-deal calculation of the VaR (as creation of the VaR takes too long to load). Secondly, nocounterparty verification is given on all trades and/or the Energy Trading and Risk Management (ETRM)platforms may not fully be able to support registration of complex deals. Thus no complete and/or accurateinformation is always available in the ETRM platforms to perform monitoring on. Third, industries andmanagement’s risk appetite grants traders a large degree of freedom, providing opportunity for increased risktaking and reducing the focus on controls.

KeywordsContinuous control monitoring framework, electricity trading, three lines of defense

DisclaimerAlthough the content of this report is prepared with the greatest carefulness, it is presented without any obligation.Both the VU University Amsterdam and the authors of this report decline any responsibility. The VU UniversityAmsterdam does not warrant for the correctness and/or completeness of facts, data, beliefs, expectations and/oroutcomes mentioned in this report. The VU University Amsterdam does not accept accountability for any damagethat results from inaccurate and/or incomplete information in this report. No part of this report may, in any formor by any means, be copied, reproduced or published without prior written permission by the VU UniversityAmsterdam.

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Content

1. Introduction .......................................................................................................................................................4

1.1 Subject...............................................................................................................................................................4

1.2 Objective ...........................................................................................................................................................4

1.3 Research question ............................................................................................................................................5

1.4 Scoping and Limitations ..................................................................................................................................6

1.5 Research methods ............................................................................................................................................6

1.6 Relevance for EDP-Audit ................................................................................................................................. 7

2 Theory .....................................................................................................................................................................8

2.1 The electricity industry in the Netherlands .....................................................................................................8

2.2 Electricity trading ............................................................................................................................................9

2.3 Risks Management......................................................................................................................................... 14

2.3.1 Electricity trading risk framework .......................................................................................................... 15

2.4 Internal Controls............................................................................................................................................ 19

2.4.1 Continuous control monitoring...............................................................................................................20

3. Analysis ................................................................................................................................................................22

3.1 Case study at an energy trading company .....................................................................................................22

3.1.1 Trade manager .........................................................................................................................................22

3.1.2 Risk management ....................................................................................................................................23

3.2 Analysis with experts .....................................................................................................................................24

3.3 Validation of framework ................................................................................................................................25

3.3.1 Validation at line management and risk management of an energy trading company .........................25

3.3.2 Validation at internal auditor of an energy trading company................................................................26

3.4 Perceived added value of the framework.......................................................................................................27

4. The framework.....................................................................................................................................................28

4.1 Structure of the framework............................................................................................................................28

4.2 The framework...............................................................................................................................................29

5. Conclusion ........................................................................................................................................................... 41

6. Considerations.....................................................................................................................................................42

7. Implications for EDP Audit .................................................................................................................................44

8. Literature .............................................................................................................................................................45

Appendix I - Scoping ...............................................................................................................................................47

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1. Introduction

1.1 Subject

In 1998 the Dutch government initiated the liberalisation and privatisation of the electricity market, followed bya decision in 2007 to split up the large electricity companies into separated companies for control of theelectricity grid and electricity supply. Deregulation was expected to “draw private investments, increaseefficiency, promote technical growth and improve customer satisfaction” (Bajpai and Singh, 2004). Thetraditional electricity market was vertically integrated and government owned (Meeus, 2006); the electricitysupplier also generated the electricity. From 2007 onwards, with companies divided and the market entrybarriers lowered, the number of electricity suppliers quickly expanded. Without monopolies and with lowermarket entry barriers, the influence of supply and demand on prices grew and therefore fluctuation of pricesand uncertainty increased. With the principles of scarcity and profitisation introduced into the electricitymarket, electricity trading has become of vital importance in both securing the delivery of electricity and as ameans to make profit. Because supply and demand have large fluctuations, monitoring of the electricityportfolio is crucial. The volume of electricity trading has increased tremendously over the past years. Theconcept of electricity trading is however still relatively young and its trading market continues to rapidlydevelop.

Internal control is an important aspect within electricity trading since trading has a high inherent risk and isfraudulent sensitive. There have been a number of cases already in which fraudulent activities in Energy tradingin the US have led to scandals and the bankruptcy of a company: Part of the fall of Enron was caused by theillegal black-outs to increase profits of Enron’s trading activities in California (e.g. McLean and Elkind, 2003).To prevent the impact of internal control failures on the trading market, and to create more transparency, lawsand regulations are created for electricity trading. In Europe an example of an upcoming regulation is EMIR,which requires to clear all over the counter trades by a certified clearing house.

Internal control is also currently going through rapid developments by making controls continuously monitored(e.g. Handscombe, 2007). Controls are no longer tested on a periodic basis but can also be tested continuously,if an error occurs this is immediately reported / repaired. This concept of continuous control monitoring forportfolio management in financial trading have been used for a number of years (e.g. Wang et al. 2002). Inelectricity trading however, no studies have been performed on continuous control monitoring in theNetherlands yet.

In this thesis we explore the applicability of continuous control monitoring in the electricity trading, creating aframework for the to-be situation of internal control for electricity traders. We will identify the risks forelectricity traders, the control objectives to mitigate these risks and the controls that can be implemented torealize the control objectives. We will focus on the continuous monitoring controls since these are supposed tobe used in a ‘mature’ internal control environment and perceived as the to-be situation in internal control.

1.2 Objective

The main scientific contribution of this thesis lies in the creation of a framework for implementing monitoringframework for electricity trading. This allows management to manage risks in a more mature way. Thisresearch tries to answer the question to which extent continuous control monitoring can be used to provideadditional insight and control over energy trading. As a practical contribution, this thesis gives the energytrading sector guidelines for creating a continuous control monitoring framework. Upcoming legislationchanges such as European Market Infrastructure Regulation (EMIR), which will be discussed in more detailonwards, can effectively be embedded in the efforts to implement the continuous control monitoringframework.

An example of the different maturity levels for internal control is described in a white paper of IBM (2005),refer to the following figure:

Figure 1: Energy trading maturity levels

In the white paper of IBM, the highest maturity that can be reached is the level 4 maturity:

“Level 4 maturity is attained only by firms with integrated organizational structures, processes, analytics andstraight-through processing which is designed to enable seamless endfrom market research to regulatory reporting.” (IBM, 2005, pp 5)

As is described in the quote above, control is also part of the maturity level of aobjective of this thesis is to create a control frameworkwith the control function of a level 4 maturity as described in the‘dashboards, real-time calculations’). The control framework on level 4 is not further elaborated in thewhitepaper. The objective of this thesis is to describeenvironment by using continuous control monitoring

1.3 Research questionCan a best practice framework for internal (continuous)defined?

In this thesis we will more specifically investigateelectricity trading market.The results will be used to determine key factors for a framework forThe following sub questions will be answered in this thesis:

Which risks can be identified in electricityo [Question to be answered implicitly: what iso [Question to be answered implicitly: what is a risk

Which controls can be identified to manage the identifiedo [Question to be answered implicitly: what are controls and which types can be identified]o [Focus on continuous control monitoring]

Using the controls identified, can a best practice frameworktrading be defined?

We note that for some risks or control objectives, a continuous monitoring control may not be desired orfeasible. The scoping is limited to defining continuous monitoring controls.control can be defined, no additional manual, IT dependent or automated controls are defined.

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In the white paper of IBM, the highest maturity that can be reached is the level 4 maturity:

integrated organizational structures, processes, analytics andthrough processing which is designed to enable seamless end-to-end trade processing and control,

” (IBM, 2005, pp 5)

e quote above, control is also part of the maturity level of an energy trading company. Theto create a control framework based on continuous control monitoring. This is in line

described in the work paper (refer to the text in the figureThe control framework on level 4 is not further elaborated in the

he objective of this thesis is to describe (part of) the to-be situation for the level 4 maturity controlmonitoring.

(continuous) control monitoring for electricity trading be

how continuous control monitoring is applied in the

The results will be used to determine key factors for a framework for internal (continuous) control monitoring.swered in this thesis:

electricity trading?[Question to be answered implicitly: what is electricity trading?][Question to be answered implicitly: what is a risk?]

Which controls can be identified to manage the identified risks?[Question to be answered implicitly: what are controls and which types can be identified][Focus on continuous control monitoring]

an a best practice framework for internal control monitoring in energy

We note that for some risks or control objectives, a continuous monitoring control may not be desired orThe scoping is limited to defining continuous monitoring controls. If no continuous monitoring

, IT dependent or automated controls are defined.

integrated organizational structures, processes, analytics and,

y control

monitoring.

for internal control monitoring in energy

1.4 Scoping and LimitationsThe thesis will focus on the current situation of electricityrefers to the trading of both raw materials used for the generation of energy, liquid gas and electricity of thegrid, we limit the scope of this thesis to electricitythat energy and electricity trading is no longer bound to individual countries, for complexity reasonsinternational trade and its complications is scoped out of the research questions.

As this thesis is written as part of the EDP-Audit, the focus will lie on identifying relevant risk and appropriatemitigating these risks by means of internal controls.cannot assure completeness of juridical and compliance risks. General legislation articles such as BW and WBPare not fully included in the scoping, as these risks are not restrictedFor practical reasons, we assume that a company has apprinformation and that all information in energy trading is businessincluded in the scope of this research. Last, General Ledger accounting is excluded from the sas we focus on risks related to trading. Please refer to appendix 1

1.5 Research methods

This research started with desk research on the most important aspects of the problem definition: Trading and risks affiliated with tradingWe investigated how trading processes are structured in general andare structured for energy trading. We looked at the information systems used and the risk frameworks thatapplied in these sectors.

Internal controls and continuous control monitoringWe investigated what continuous control monitoring

The second part of our research is based on case studiesWe used multiple cases studies technique with an exploratory character (based on descriptions of BaxterJack, 2008). We performed multiple case studies at one energy trading company with representatives ofstakeholders that will be affected by the continuous control monitoringthe trade manager, risk management and internal auditperformed case studies with subject experts on financial trading and energy trading.

After creation of theframework, first, second andthird line employees of energysuppliers were asked tovalidate the framework. Thefeedback of all reviewers wasused to improve theframework.

Based on the desk researchperformed, the data gatheredfrom our case studies andvalidation we established abest practice internalcontinuous controlmonitoring framework forproactively managing risks inelectronic trading.

Figure 2: Thesis structure

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electricity trading in the Netherlands. Whereas energy tradingboth raw materials used for the generation of energy, liquid gas and electricity of the

on the grid. In addition, although an argument can be madetrading is no longer bound to individual countries, for complexity reasons

is scoped out of the research questions.

Audit, the focus will lie on identifying relevant risk and appropriatemitigating these risks by means of internal controls. We aim to provide an overview of all relevant risks, but

juridical and compliance risks. General legislation articles such as BW and WBPare not fully included in the scoping, as these risks are not restricted to energy trading, or trading in this matter.For practical reasons, we assume that a company has appropriate procedures in place regarding personalinformation and that all information in energy trading is business-to-business related. Therefore, privacy is not

Last, General Ledger accounting is excluded from the scope of the thesisPlease refer to appendix 1 – scoping for additional scoping information.

with desk research on the most important aspects of the problem definition:

how trading processes are structured in general and more specifically how trading processesat the information systems used and the risk frameworks that are

continuous control monitoringcontinuous control monitoring is and how it is applied in businesses.

studies at an energy trader company and with subject experts.multiple cases studies technique with an exploratory character (based on descriptions of Baxter and

ies at one energy trading company with representatives of allcontinuous control monitoring control risk framework. We approached

internal audit of an energy trading company. Additionally we haveperformed case studies with subject experts on financial trading and energy trading.

Thesis structure

juridical and compliance risks. General legislation articles such as BW and WBPthis matter.

business related. Therefore, privacy is notcope of the thesis

scoping for additional scoping information.

are

.

approached

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1.6 Relevance for EDP-AuditThis thesis is written as part of the EDP-Audit post graduate program of the Vrije Universiteit Amsterdam.Implicitly the whole EDP program has contributed to our thesis by laying a foundation of our EDP auditingknowledge. During the program we have learned how EDP audits are performed and what is expected of EDPauditors. In addition, the concepts of risks, control objectives and control were actively discussed. Thisknowledge was for example used by the scoping of this thesis and creating the research question. We havehighlighted a number of modules to depict explicitly the link of our thesis with the EDP auditing program:

Module 1 – ‘BIV A/O’ provides an introduction into the IT auditing and described the administrativeorganisation. As described in this module the primary goal of an (IT) audit is the reduction of uncertainties. Toperform an audit, objectives are needed that describe the ‘soll’ position (to be). The link with this thesis is thatthis thesis described the ‘soll’ position by defining control objectives to reduce uncertainties. Also in thismodule the difference of IT general controls and application controls is discussed (but this was also discussed infor example module 2.4 – ‘Inrichting en audit van het beheer’). In this thesis effective IT general controls areconsidered a prerequisite for the creation of a framework, but are not the primary scope, refer to paragraph2.1.3.8 for more information on the de-scoping of IT general controls.

Another important link with this module is the link with the book of Starreveld et al. (2008) on administrativeorganisation. This link between an EDP audit and the administrative organisation is of major importance forEDP auditors and has also discussed during other modules in the program; e.g. module 2.1 – ‘IT Auditing ‘andmodule 3.2 – ‘synthese audit aanpak, techniek & AO/IC’.

As described in Starreveld we first need to the describe the organisational processes to be able to identify risksand create the control objectives. Electricity traders can be mainly categorized as a trading company, based onthe typology of Starreveld (2008). The main objective of electricity traders is to add value by buying and sellingelectricity. The generation of electricity is for this typology seen as external party delivering the ‘input’ for thetraders processes. In trading companies Starreveld described that the Segregation of Duties (SoD) is of majorimportance. In this thesis we have used the front, middle and back office to describe the SoD. Electricity traderswhen trading in financial derivatives also have characters of the typology ‘financial organisations’. Thederivatives in which electricity traders trade are however still based on the ‘product’ electricity (proprietarytraders are left out of scope, refer to paragraph 2.2.1), so the main typology of ‘trading company’ will be used inthis thesis.

Please note that the modules mentioned in this paragraph are not the only relevant modules in relation to thisthesis. The other modules also have a link with this thesis, but the link is less explicit.

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2 TheoryIn the following paragraphs, the theoretic framework that formed the basis for our case studies and controlframework is constructed. First, we dive into the electricity market in the Netherlands, discuss what constituteselectricity trading and define the basic structure of an electricity trading company. Afterwards, a risk frameworkconcerning electricity trading is constructed.

2.1 The electricity industry in the NetherlandsIn 1884 Sir Charles Parsons invented the steam turbine, which changed the way electricity can be utilized. Thesteam turbine could generate electricity continuously as a static value. Because a

continuous supply of electricity was made possible, the first electricity grids were created.

To understand the complexity of electricity trading, we first must understand the complexity of the nature ofelectricity itself and the transportation of electricity. To transport electricity continuously, which is necessaryfor a reliable supply of electricity, there must be a difference in ‘electric potential’ (voltage) across thetransmitting line. Electric current follows the paths of all resistances in proportion to their conductance. Thusmanipulating the electric potential or conductance allows the electricity (or ampere) to be transported. Thelevel of the voltage is dependent on the ampere, since the amount of ampere influences the electric potential.

To further simplify, electronic transmission can be compared to a river. The height of the water can be seen asthe voltage, the amount of water entering and leaving the river the ampere. Fast flowing rivers with a low waterlevel could thus be seen as having a high ampere and low voltages. If the inflow of the water rises while theoutflow stays the same this will rise the water level. The same is applicable for the outflow and dropping ofwater level. If the water level is very high the river could overflow or even break. This example is also applicablefor the transmitting of electricity, if the inflow or outflow of amperes fluctuates, this could affect the voltagelevel. If demand and supply do not match at a certain time congestion can occur which results in a black out(e.g. Bajpai and Singh, 2004).

It is thus very important that the electricity markets are monitored on input and output and thus the voltagelevel. In order to have a transparent monitored market, we will first describe the different types of Dutchplayers in the electricity market:

Manufacturer: The manufacturer generates the electricity using for example steam turbines. This generatedelectricity is the ‘inflow’ of electricity on the electricity grid.

Regional grid net operator (RGO): This is the party that maintains the electricity grids used for transportingthe electricity.

Electricity supplier: The electricity supplier sells electricity to end users which supply the electricity. This isthe ‘outflow’ of electricity.

Congestion party: This is the party that balances the ‘inflow’ and ‘outflow’ of electricity to maintain theamount of voltage predefined. In the Netherlands for example currently the high voltage lines to transportelectricity are 380 000 volt, which is reduced to 240 volt for the electricity coming out of the connectionswith the houses in the Netherlands.

In the Netherlands Tennet has the monopoly as the congestion party for the Dutch market (for 110 volts andhigher)1. There are around 8 RGOs (e.g. Alliander, Enexis and Stedin) which all have a monopoly in a certainpredefined area. There are multiple suppliers and manufacturers, a great number of manufactures are alsosuppliers (e.g. Nuon, Eneco and Essent). However, not all electricity suppliers generate energy (e.g. NEM,Oxxio and Atoomstroom) and thus these suppliers have to obtain electricity by trading.

The legislation ‘wet onafhankelijk netbeheer’ dating from 2011, obliged suppliers/ manufactures to be separatedfrom the regional grid net operator. Court rulings from 2012 ruled the legislation invalid2. The majority of

1 http://www.tennet.org/images/animatie_2009_10_01_tcm41-18485.swf , accessed latest 26-08-122 http://www.rijksoverheid.nl/documenten-en-publicaties/kamerstukken/2012/03/13/kamerbrief-over-de-uitspraak-van-de-hoge-raad-over-wet-onafhankelijk-netbeheer.html , accessed latest 26-08-12

parties already complied to this legislation, but several RGO’s did not complete the split up.electricity market there are thus regional grid net operatorssupplier/ manufacture.

Another aspect of the Dutch electricity market in the Netherlands is that the electricity grid is connected to theelectricity grids from Belgium, Germany, Norway and the UK. This makes it possibelectricity with these other countries.

2.2 Electricity tradingAs stated in the paragraph above, electricity is traded to buy or sell electricity. However the ‘inflow’ and the‘outflow’ should be matched by Tennet. For the electricon the net immediately but have to announce their plannedsuppliers fail to supply the amount of electricity that they announced to Tennetconsume more electricity than agreed (this is only the case for industrial companies since they have aconsumption large enough to affect the balance), Tennet has to restore the balance by switching on powerplants or by asking clients to reduce their electricity consumption. The company causing this unbalance has topay unbalance prices which are far higher than the market pricesunbalance prices due to a mismatch in supply and demandin more detail in paragraph 2.3.1.

An example of the differences in prices between regular market prices and unbalance prices for electricity aredepicted in the next graph. This graph shows the avof the year 2009 on the APX broker platform. The secondmonth for compensating the unbalance in electricity (unbalance price).the price of the average electricity price was paid. In case of unbalance the supplier thus has to pay more than30 times the price for which he could have purchased electricity. The last column depicts the average unbalanceprice of the concerning month. As can be seen on average suppliers have to pay at least double thenormal electricity for unbalance situations. Suppliers and other companies thus want to make sure they buy andsell enough electricity on the market to prevent they have to pay unbalance prices

Figure 3: unbalance prices

3 http://www.tennet.org/bedrijfsvoering/Systeemgegevens_afhandeling/verrekenprijzen/index.aspxlatest 26-08-12

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parties already complied to this legislation, but several RGO’s did not complete the split up. In the Dutchelectricity market there are thus regional grid net operators that still are only RGO, but some also still are

Another aspect of the Dutch electricity market in the Netherlands is that the electricity grid is connected to theelectricity grids from Belgium, Germany, Norway and the UK. This makes it possib le to supply / demand

above, electricity is traded to buy or sell electricity. However the ‘inflow’ and the‘outflow’ should be matched by Tennet. For the electricity traders this implies that they cannot supply electricity

heir planned electricity production (supply) at Tennet. Ifthat they announced to Tennet, or in case buyers of energy

more electricity than agreed (this is only the case for industrial companies since they have aTennet has to restore the balance by switching on power

their electricity consumption. The company causing this unbalance has topay unbalance prices which are far higher than the market prices 3. The risk of financial loss due to payingunbalance prices due to a mismatch in supply and demand is called Physical delivery risk. This will be discussed

An example of the differences in prices between regular market prices and unbalance prices for electricity arethe average electricity price (per megawatt/hour) for each month. The second column depicts the highest price paid to Tennet that

electricity (unbalance price). For some months more than 30 times. In case of unbalance the supplier thus has to pay more than

30 times the price for which he could have purchased electricity. The last column depicts the average unbalanceconcerning month. As can be seen on average suppliers have to pay at least double the price of

. Suppliers and other companies thus want to make sure they buy andhey have to pay unbalance prices.

www.tennet.org/bedrijfsvoering/Systeemgegevens_afhandeling/verrekenprijzen/index.aspx , accessed

Another aspect of the Dutch electricity market in the Netherlands is that the electricity grid is connected to the

electricity

This will be discussed

hour) for each monthcolumn depicts the highest price paid to Tennet that

30 times the price for which he could have purchased electricity. The last column depicts the average unbalance

. Suppliers and other companies thus want to make sure they buy and

, accessed

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Another aspect that has influenced electricity trading is deregulation. As stated above; where in the past in theNetherlands most energy companies were integrated companies (supplier, manufacturer and grid net operator)due to the ‘wet onafhankelijk netbeheer’ most companies have become specialized companies. These companiesthus are either the supplier or the manufacturer. Since there are multiple manufacturers and suppliers, thevolumes of electricity trading has increased and individual traders are becoming more active. The deregulationcausing more active trading has made the electricity market more like a large financial market (Edwards,2009). Effectively, the electricity market has therefore been commoditized. Once goods and services enter theworld of commodity markets, they become part of the trading system (Stagliano and Emerson, 1997). Recentevents have demonstrated how the price of electricity can be highly volatile, and that electricity prices arenecessarily dependent on a multiplicity of factors. In some cases these are totally external to the traditional playof supply and demand. In electricity, gas and emissions markets across the European Union price now dependsin part on unpredictable policy decisions, rapidly changing regulatory rules and imperfectly harmonizedtransmission access mechanisms.

As such, the energy (and thus more specific) electricity market is a collection of interrelated business focused ondelivering electricity and heating fuel to consumers (Edwards, 2009, p. 2). As stated earlier, energy tradingconsists of three main sources/stages, namely raw materials, refined materials and produced electricityavailable on grid. While trading in all three commodities is possible, the scope of this thesis is limited toelectricity on the grid. Including refined and raw materials in the scope of the thesis would place a largeemphasis on the physical delivery of the goods, which although complex, is predominantly a logisticaloperation and should be managed and audited as such.

2.2.1 How does electricity trading work?

Electricity trading requires at least three components for trading; suppliers of electricity, buyers of electricityand a provider of a platform for trading. Although the difference between buying and selling energy isstraightforward, the role of a buyer or seller in trading is not. On the electricity market there can be variousparties playing different roles in different situations. One company can for example be both a buyer and a sellerof electricity: e.g. Banks, Pure Traders etc. These traders do not (or limitedly) generate or supply electricity butearn money by speculative trading of electricity. Also suppliers of electricity can sell part of their electricity butat the same time buy energy for hedge purposes.

In general three types of traders can be identified, namely supply chain traders, asset back traders andproprietary traders:

Supply chain traders are the group of traders that need to buy commodities for the supply chain to functionand could also sell commodities as a result of their supply chain. An electricity producer for example needs tobuy coals for its power plant to produce electricity and can then sell the electricity. The supply chain trader hasa fixed number of commodities to buy or sell since the input and output of the supply chain is predictable.Supply chain traders trade to get the best prices for the buying / selling of the commodity. They normally takelittle risk and are bound to amounts predefined which they have to buy and sell.

Asset back traders are traders that also need to buy / sell commodities for their supply chain to function butcan also decide to ‘switch’ to another supply chain. An electricity producer for example can decide to buycommodities and produce electricity to supply to its clients, but when the price is right, also decide to buyelectricity from another electricity producer and switch of its own power plant. Asset back traders still have‘assets’ to produce the electricity but can also decide to deliver the electricity purely by trading. In theNetherlands most electricity producers are asset back traders who have long term contracts with their clientsand thus by trading decide the optimum between buying or producing electricity. The practice of takingadvantage of a price difference between two or more markets (e.g. coals versus electricity) is called arbitrage.

Proprietary traders are the traders that do not have assets or contracts which they have to support. In factproprietary traders do not desire physical delivery, but purely aim at making money by trading. They are notbound to certain commodities but can trade in anything. This exposes the company to large risks, since if thetrader cannot sell a forward (explained in the next paragraph), the company might be forced to by the contractto and obtain the traded commodity, which they cannot use. Proprietary traders speculate on fluctuation inmarket prices on the expectations that prices will go up or down (not bound to supply restrictions). This iscalled directional trading.

The different types of traders are depicted in the next figure:

Figure 4: types of trading and their affiliated aspects

In this thesis we will focus on the Supply chain traders and Asset back traderselectricity. Proprietary traders are out of scope for our risk framework since they are not limited by orcharacteristic for the energy market. Please note that this does not mean that the framework is not applicablefor proprietary traders, since a large number of the r

2.2.2 Markets

As described before, electricity on grids have to be balanced and have to be communicated to the congestionparty. The trading of electricity focuses thus on the future demand/supply of electricity. Since traders trade in afuture amount of electricity, they trade in derivatives. Athat specifies conditions (especially the dates, resulting valuesamounts) under which payments are to be made between the parties

In order to trade electricity two (main) kinds of (derivative)the forward and the spot market. One market is aimed at the trading ofthe right of buying electricity in the future.

The spot market gives a company the right to buy (call option) oron a set future date for a set price. This only concerns theselling the amount of electricity. An option thus giveelectricity in the future for a set price (hence the name)does not have the obligation to buy or sell the electricitymoney. In a highly volatile market options can be of high value since you as option holder can buy electricity fora predefined price while the market price might behave an option to buy electricity for a price that is much higher than tthis option. You then loose the option if the set day of the option is passed.risk since the option to buy costs money which, after issue date, might be not worth the benefits.operational risk will be further explained in paragraph

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The different types of traders are depicted in the next figure:

Supply chain traders and Asset back traders in the energy market producingare out of scope for our risk framework since they are not limited by or

for the energy market. Please note that this does not mean that the framework is not applicable, since a large number of the risks in energy trading are generic trading risks.

As described before, electricity on grids have to be balanced and have to be communicated to the congestionthus on the future demand/supply of electricity. Since traders trade in a

future amount of electricity, they trade in derivatives. A derivative instrument is a contract between two partiesthat specifies conditions (especially the dates, resulting values of the underlying variables, and notionalamounts) under which payments are to be made between the parties (Rubinstein, 1999).

(derivative)markets are available (e.g. Bajpai and Singh, 2004);One market is aimed at the trading of electricity, the other market aimed at

the right to buy (call option) or sell (put option) a certain amount of electricity. This only concerns the right to buy/sell, there is no obligation of buying or

An option thus gives the buyer the option to buy or sell a certain amount of(hence the name). These pose a low risk to the end buyer since the buyer

does not have the obligation to buy or sell the electricity. The option itself however costs a certain amount ofons can be of high value since you as option holder can buy electricity for

be much higher. It can also be the other way around, if youhave an option to buy electricity for a price that is much higher than the market price, you do not want to usethis option. You then loose the option if the set day of the option is passed. There is only a limited operationalrisk since the option to buy costs money which, after issue date, might be not worth the benefits. The concept of

paragraph 2.3.1.

thus on the future demand/supply of electricity. Since traders trade in aderivative instrument is a contract between two parties

;

city

ons can be of high value since you as option holder can buy electricity for

he concept of

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The forward market gives a company, when buying or selling, the obligation to buy or sell the amount ofelectricity at a certain point in the future. Forwards have an expiration date when the (fixed) amount ofelectricity has to be supplied. A buyer can sell the right to buy the forward, a supplier can sell the right to supplythe energy. The date and amount of a future cannot be changed while trading. Forwards pose a higher riskbecause the electricity has to be bought for a certain price in the future. This contractual agreement is calledlegal (contractual) risk and will be explained in more detail in paragraph 2.3.1. Similar to a forward is a future;this future contracts does not have a fixed time of supply but a variable time of supply (can be agreed by thebuyer and seller)

There are two methods of trading in these markets: via an exchange or Over The Counter (OTC). Trading via anexchange implies that the trader and seller of a forward / spot are connected to an exchange. The deal is settledon-, and registered by the exchange and will be mostly automatically be cleared (for clearing refer to the sectionbelow). Trading over the counter implies that the buyer settles a deal directly with the seller. An exchange couldbe used to find a counterparty, but not to settle or register a deal. We note that counterparties are not alwaysknown when trading via an exchange. Some exchanges only shows approved counterparties, but does soanonymously.

2.2.3 Clearing of a trade

Once a trade has been settled by the buyer and the seller, the trade can be handed over to a clearing house,which then clears the trade. This can be either on an exchange or in the OTC markets. Clearing the trade meansthat the clearing house steps between the two original traders clearing firms and assumes the legal counterpartyrisk for the trade. Both the buyer and seller have to be registered at the clearing house and have to deposit awarrant to guarantee that they can pay (part of) the deal. The buyer and seller do not have a contract with eachother, but both have a contract with the clearing house. If one of the counterparties fails to pay (credit risk) theclearing house will still pay to the other counterparty. We do note that this guarantee impacts the liquidity riskof the trader. The process of transferring the trade title to the clearing house is referred to as “novation”.Clearing houses have to be accredited by the AFM in the Netherlands4, the risk that an clearing house will gobankrupt (and thus that they cannot fulfil the payments as agreed) is therefore limited.

The warrant (in the industry this is called margin) that buyers and sellers have to deposit is not a fixed amountat all time. Since the market is volatile and the perceived value of a trade can vary, but also since trades mightbe expired and new trades traded, clearing houses can ask for more or less margin of counter parties on aperiodic basis. Buyers and sellers thus might have to deposit more margin or receive margin back from theclearing house. This has, as described earlier, impact on the liquidity risk of the trader (all the moneyoutstanding as margin cannot be used for trading). Hence, apart from the risk management model itself,frequency of margin computation becomes an important factor in determining the level of protection which aclearing house derives from its margining model (Kumar and Sami, 2004).

2.2.4 Electricity trading software

Traditionally traders in the energy market traded during personal meetings and/or telephone calls. As statedbefore, due to changes in legislation and other factors, there have become high numbers of electricity suppliersand buyers. To keep an overview of all the supply and demand, software programs have been developed tosupport the trading. Exchanges can facilitate the trading in electricity. These exchanges provided an electronicplatform on which trading can take place.

Exchanges (e.g. APXENDEX in the Netherlands) can provide web based applications in which trades can besettled. However, because the trades settled have to be documented and the trade information might be neededthroughout the company (e.g. for forecasting purposes), most energy traders have their own applications inwhich they trade. These applications are connected to the platforms and deal information is captured in thesesystems. The common name for electricity trading software is ‘Energy Trading and Risk Management (ETRM)Platforms’ (Gartner, 2011). These ETRM platforms are not limited to electricity but provide the opportunity to

4 http://www.afm.nl/en/professionals/afm-voor/handelsplatform-beurzen/clearinginstelling.aspx , accessedlatest 26-08-2012

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trade in all kind of commodities (e.g. coals, CO2 certificated etc). ETRM platforms can be limited to trading, butcould also cover functionalities for e.g. risk management (what is my exposure), accounting (posting trades),forecasting etc.

2.2.5 Trading company structure

In order to create a control framework a basic understanding of the structure of energy trade companies isneeded. The Generally Accepted Risk Principles5 (GARP) state that there must be clear segregation of dutiesand reporting authority between the Front-, Middle- and Back-Offices up to the Board level of the organization.Although this is an standard from the US, based on our experience with energy trade companies we noted thatthis principle is widely applied under energy trading companies.

The Front-Office is where the actual trades are made. Traders initiate transactions and execute trades withcounterparties. By trading electricity, traders create market risk (market risk is explained in the nextparagraph) for the company. Traders (supply chain/asset back traders) normally receive a number of positionsfor which they have to trade on a day on a trade book. They then scan the market on the best traders for thesepositions. The motive of the Front-Office is profit creation, risk management and controls is not their mainfocus.

The Middle-Office is responsible for measuring and monitoring the risks created by the Front-Office andoperations. Operations is in short the ‘ongoing recurring (cyclic) activities’ that are performed to keep thebusiness running (e.g. for a bakery, baking bread is his operation). In an pure trade company, trading is therecurring business to add value. The Middle-Office motive is to mitigate and control risk for the company andto support decision-making through, e.g. analytical (profit/risk) reports and price forecasting (forward curve).They normally create the trade books that the traders use and make calculation for the exposure of the companyto risk. This can for example be by calculating the value at risk (VaR) for all trade books. The VaR is thecalculation of the hypothetical profit-and-loss probability density function. Simply stated: the electricity marketis a volatile market. By trading you expose the company to risk that you make inefficient trades and thereby losemoney. The VaR answers the question, "What is my worst-case scenario?" or "How much could I lose in a reallybad month?". A VaR statistic has three components: a time period, a confidence level and a loss amount (orloss percentage).

The Back-Office is responsible for confirming transactions, accounting, bookings, settlements, payments and soforth. The back-office only processes the trades made internally and make sure for example that the paymentsare done as agreed in trades.

The roles of the front / middle / back office are linked to the stages in the trade process in the figure on the nextpage.

5 http://www.garp.gov , accessed latest 26-08-2012

Figure 5: roles of Front-Office, Mid-Office and Back-Office

For this study we will focus on control on all three levels of the organization.

2.3 Risks Management

ISO 31000 (2009)6 defines risk as the 'effect of uncertainty on objectives'. These uncertainties consevents (which may or not happen) and uncertainties caused by ambiguity or a lack of information. It can entailboth negative and positive impacts on objectives.load demand and production cost provide means for opportunity and risk (Liu Wu and Yixinand Yixin define risk in the energy market as the hazard to which a market participant is exposed because ofuncertainty. To manage uncertainty and risk, risk management is emanagement is the process of achieving the desired balance of risk and return through a particular tradingstrategy.

The three lines of defence model can be used to manage risks within companies.several legislations such as the Dutch Corporate Goverance Code, Basel and Solvency prescribes that a threelines of defence model is embedded within the organisation andThe lines are7:

First line of defence: management control Second line of defence: risk management / compliance Third line of defence: Internal audit

The Sarbanes–Oxley Act of 2002 (SOX) had a major influence on the three line of defence model. Thismade binding for companies listed in the United States on an exchange. Section 404 of SOX entails thatmanagement is also required to produce an “internal control report”, thus management testing (first line) gotmore emphasis for US listed companies (Alles et al

For the energy trading risk framework, we embed the three lines of defence model as described for FinancialServices into the framework. For an energy trading company, the following

6 http://www.iso.org/iso/catalogue_detail.htm?csnumber=431707

http://www.accountant.nl/readfile.aspx?ContentID=34556&ObjectID=316784&Type=1&File=0000035052_Meetlat.pdf , accessed latest 26-08-2012

14

his study we will focus on control on all three levels of the organization.

defines risk as the 'effect of uncertainty on objectives'. These uncertainties cons ist of bothevents (which may or not happen) and uncertainties caused by ambiguity or a lack of information. It can entail

ositive impacts on objectives. Uncertainties in the energy market such as production price,s for opportunity and risk (Liu Wu and Yixin, 2006). Liu Wu

define risk in the energy market as the hazard to which a market participant is exposed because ofuncertainty. To manage uncertainty and risk, risk management is embedded in trading companies. Riskmanagement is the process of achieving the desired balance of risk and return through a particular trading

used to manage risks within companies. For financial institutions,several legislations such as the Dutch Corporate Goverance Code, Basel and Solvency prescribes that a threelines of defence model is embedded within the organisation and that these lines have to function separately.

anagement control/line managementisk management / compliance

(SOX) had a major influence on the three line of defence model. This act wasmade binding for companies listed in the United States on an exchange. Section 404 of SOX entails thatmanagement is also required to produce an “internal control report”, thus management testing (first line) got

et al. 2006).

For the energy trading risk framework, we embed the three lines of defence model as described for Financialtrading company, the following roles can be defined.

http://www.iso.org/iso/catalogue_detail.htm?csnumber=43170 , accessed latest 26-08-2012

ID=34556&ObjectID=316784&Type=1&File=0000035052_M

events (which may or not happen) and uncertainties caused by ambiguity or a lack of information. It can entailthe energy market such as production price,

act was

ID=34556&ObjectID=316784&Type=1&File=0000035052_M

Figure 6: Three lines of defense model

The third line is occupied by the internal auditinternal auditing as follows (Bariff, 2003, p. 4):

Internal auditing is an independent, objective assurance andadd value and improve an organization's operations. It helps an organization accomplish itsobjectives by bringing a systematic, deffectiveness of risk management, control, and governance processes.

At this point, no distinction is made between Internal and External auditmore elaborate in the discussion. Within the three Lines omodeled to the specific company can be implemented. In the nextan energy trader. It is important to note that the third line does not have a role in operational dayactivities, but audits the effectiveness of the first two lines. As such, no primary roles and responsibilities areawarded to the third line in our framework.

2.3.1 Electricity trading risk frameworkIn paragraph 2.1 and 2.2 we explained electricity, electricity trading and the structure of trading companithese paragraphs we mentioned several risks affiliated with electricity trading, such as physical delivery risk,credit risk, market risk and operational risk. To be able to applytrading it is important first to have insight in where monitoringdescribed earlier, continuous control monitoring is a method tocontrol framework we first need to identify the risks applicable in energy trading.likelihood of the risks can vary for each company, during the case studies a general consensus was reachedregarding the risk levels of the risk areas. The risks areresearch question. The risk area’s will be below.

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Line management is considered the first line. The firstline is directly responsible for risk management andcontrol within her processes. The first line of anenergy trading company consists of the tradingmanager (and traders), Mid-Office manager (andemployees) and Back-Office manager (andemployees). The roles are defined in paragraph 2.2.5structure of an energy trading company.

Second line are departments that support the first lineand are responsible for drafting policies for the firstline. In addition, the second line monitors adherenceto the policies and applicable legislation. For thesecond line, we define Risk Management,Compliance and Legal. Compliance and Legalfocus predominantly on compliance to contractualagreements and compliance to regulatoryrequirements, thus effectively managing legal risk andregulatory risk, which are explained in paragraph2.3.1.

department. The Institute of Internal Auditors (IIA) defines

Internal auditing is an independent, objective assurance and consulting activity designed toorganization's operations. It helps an organization accomplish its

disciplined approach to evaluate and improve thecontrol, and governance processes.

Internal and External audit. The role of both parties is explainedWithin the three Lines of defense model, a key risk control framework

modeled to the specific company can be implemented. In the next paragraph, we define such a framework forat the third line does not have a role in operational day-to-day

activities, but audits the effectiveness of the first two lines. As such, no primary roles and responsibilities are

risk framework2.1 and 2.2 we explained electricity, electricity trading and the structure of trading compani es. In

several risks affiliated with electricity trading, such as physical delivery risk,To be able to apply continuous control monitoring on energy

trading it is important first to have insight in where monitoring is applicable and beneficial for the company. Asis a method to be able to mitigate risks. In order to create a

control framework we first need to identify the risks applicable in energy trading. Although the impact andrisks can vary for each company, during the case studies a general consensus was reached

regarding the risk levels of the risk areas. The risks are discussed from largest to lowest risk in relation to our

Line management is considered the first line. The first

Second line are departments that support the first line

and

es. In

is applicable and beneficial for the company. As

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Risk Reference DescriptionCredit risk 2.2.3 The risk of financial loss due to counterparties not

being able to pay their obligations.Market risk 2.2.5 The risk of financial loss due to changing

circumstances in the market.Liquidity risk 2.2.3 The risk of suffering loss due to costly conversion of

illiquid assets into cash.Operational risk 2.2.2 The risk of financial loss due to ineffective internal

controls .Physical delivery risk 2.2 The risk of financial loss due to unbalance prices as a

result of a mismatch between agreed and deliveredsupply.

Regulatory risk 2.3 The risk of financial loss due to non-compliance withlaws and legislation.

Legal (contract) risk2.2.2 The risk of financial loss due to non-compliance with

contractual requirements.

Table 1: risks affiliated with trading

2.3.1.1 Credit risk

Energy trading companies can buy and sell energy of other companies. In case an energy trading buys electricitythere is a physical delivery risk as described above. In case an energy company sells electricity, there is a riskthat the counterparty is unable to pay, or unable to perform on future obligations. This risk increases in fast-changing markets. If the counterparty is able to pay but not in time this can affect the liquidity of the companywhich can lead to the risk of not having enough credit to continue business processed (Denton et al., 2003).This risk mostly has a domino effect and was one of the causes of the fall of Enron (Longstaff et al., 2005).

2.3.1.2 Market risk

Customers of energy companies mostly have contracts with pre-defined energy rates. But to generate electricitymostly coal and oil is needed (world energy outlook 2010). Energy companies are therefore highly dependenton the commodity prices for the production of energy (Edwards, 2009). This exposes the energy company to themarket risk; the risk of financial losses due to fluctuations in market prices. This is a basis risk for all companiessupplying on predefined rates.

To make the market risk more transparent the ‘vega’ is used. A portfolio can be of a certain value, but due to themarket changes could in/decrease in value very fast (volatility). Especially options (spot market) are verysensitive for prices changes and thus make a portfolio value very volatile. A calculating model showing howsensitive the portfolio is to market changes (e.g. what happens if the electricity price rises?) is called ‘vega’(Edwards, 2009). This model gives insight in the volatility risk of a portfolio. The company can then decide ifthis volatility is in line with its risk appetite or has to be changed.

An additional aspect of market risks are the so called ‘black swans’. A black swan refers to unexpected event(outlier) of large magnitude and consequence8. A black swan is thus the occurrence of a big, unlikely andunforeseen event with a large impact. An example of a big event related to electricity trading is for instance the9/11 terrorist attack on the Twin Towers, which led to invasion of Iraq, which in turn spiked oil prices, thushaving an impact on electricity prices. Possible big events may be political and/or geographical instability orbreakthrough inventions, but the main characteristic of a black swan is that it’s unexpected. The black swan isvery much related to tail risk. Tail risks refers to low probability events that have a disproportionate impact onprices9. Tail risks also have a low probability, but are can be foreseen to some extent. An example is theincreasing tension in Strait of Hormuz at Iraq, which may have a future impact on oil prices.

8 http://www.nytimes.com/2007/04/22/books/chapters/0422-1st-tale.html?_r=1 , accessed latest 26-08-20129 http://www.ft.com/cms/s/0/74465e8c-35ec-11e1-9f98-00144feabdc0.html#axzz23SMeE9T8 , accessed latest26-08-2012

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2.3.1.3 Liquidity risk

As discussed in paragraph 2.2.3, novation decreases the credit risk, but increases the liquidity risk. Liquidityrefers to the degree in which assets can be converted into cash within a short timeframe. Assets are consideredto be high-quality liquid assets if they can be easily and immediately converted into cash at little or no loss ofvalue (Basel III, 2010, p. 5). Deposits and guarantees at clearing parties effectively reduce the liquidity of theorganizations financial situation and reduce available working capital. As such, the chance of not being able topay short term obligations increases. When an organization cannot comply with its obligations, the companyeither has to attract funding against unfavorable terms or goes default. As such, liquidity should be adequatelymanaged and poses a high inherent risk for trading companies in general.

To have insight in the liquidity risk traders can use the ‘liquidity at risk’ (LaR) calculations. The LaR is astochastic model quantifying liquidity risk by predicting the likelihood of a trader becoming insolvent due toliquidity shortage over a given time frame.10

2.3.1.4 Operational risk

Energy companies also have to face the risks that losses are caused by unexpected operational events.Operational risks are very broad and diverse. This can include system / asset failures, management changes,breakdown in internal controls, inaccurate models and even fraud. Operational risk is generic in nature but thespecifics will vary per company (Panjer, 2006). For energy trading operational risk management is recognizedas a vital part of the risk management framework (Panjer, 2006). In energy trading the operational risks can beclassified in the following main categories:

People

Since people are no computers, unexpected actions could occur. This includes human errors, lack of skilledemployees and fraud of employees.

Systems

Based on all the risks described above the energy companies creates / makes use of valuation models tomitigate the risks. For the credit risk for example complex models are used to determine the demand to beobtained via energy trading (Weron, 2000). These models are a simplification of real life. But because ofthis, there is a risk that models do not work correctly (Edwards, 2009). If models do not work correctly,wrong / inefficient trades could be made.

Information technology is also a big cause for operational risks; if systems are not available or do notprevent fraud operations might be harmed. Energy trading companies rely heavily on energy tradeapplications for their performance (Bajpai and Singh, 2004). Also continuous control monitoring asdescribed in this study relies on IT (Alles et al., 2006), therefore this part of operational risk providesopportunities for continuous control monitoring to be applied.

Process

There are risks that the transactions cannot be performed. This could include execution errors or evenwrong contracts.

Assets

Electricity companies rely heavily on their assets for transport. An operational risk is the breaking of forexample electricity cables. Derivatives can also been seen as an ‘asset’. Buying an option which loses itsvalue due to market prices is also another example of an operation risk related to its ‘assets’.

10 http://www.ermsymposium.org/2011/pdf/Farooqui.pdf , accessed latest 26-08-2012

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2.3.1.5 Physical delivery risk

Electricity trading has to deal with the added complexity of physical substance, which cannot simply bemanufactured, transported and delivered, at the press of a button (Weron, 2000). Companies need to knowexactly the supply and demand of energy to perform netting (Purchala et al., 2003). There is a risk that physicalflows do not occur as agreed which will lead to the company not being able to deliver in line with obligations.The congestion party will have to maintain the balance on the electricity grid and could give the non-deliveringparty penalties (e.g. the unbalance prices). The physical delivery risk can even lead to power blackout becausethe demand is higher than the supply which makes netting impossible (Carreras et al., 2004). When noelectricity can be delivered as agreed this also has effect on the electricity supplier not making money on itsassets anymore.

Another part of the physical delivery risk is the nature of electricity. Electricity is hard to store and loses itsenergy when transported. Transportation is also limited to power cables. When trading electricity is thusimportant to take into account the location of the supply (Dahlgren, 2003) to avoid the possibility that thesupply cannot be transported (with a loss).

Physical delivery risk is also named ‘performance risk’ since the company not delivering is ‘not performing asagreed’. For supply chain traders, this risk is described as having a large financial impact when the companiesassets cannot produce electricity, as instead of own generation, additional sources of electricity have to beaddressed.

2.3.1.6 Regulatory risk

As stated before, the energy market is currently being deregulated but is still bound to a number of governmentregulation in Europe (Meeus, 2006). This exposes energy companies to the risk of non-compliance and ofregulatory changes. This includes for example environmental regulations and market regulations. In theNetherlands the energy market is restricted by the regulations set by the government11, regulations include forexample the duty not to exclude customers from providing electricity12. Currently environmental regulationsstarting to have a bigger impact on energy companies (Bechberger and Reiche, 2009).

For energy trade companies in the Netherlands a number of regulations and laws apply. One example is thecompliance with the IFRS standards for example (part of) IAS 3913 and IFRS14 7, 9 and 13. These standards areaimed at the booking of the results in the annual account and will not be further elaborated in this study sincewe focus on risks affiliated with electricity trading. A regulation that is implemented on short term and doeshave an impact for energy traders within the Netherlands is the European Market Infrastructure Regulation(EMIR). This regulation described:

“All standardised OTC derivative contracts should be traded on exchanges or electronic trading platforms,where appropriate, and cleared through central counterparties by end 2012 at the latest. OTC derivativecontracts should be reported to trade repositories. Non-centrally cleared contracts should be subject to highercapital requirements. We ask the FSB and its relevant members to assess regularly implementation andwhether it is sufficient to improve transparency in the derivatives markets, mitigate systemic risk, andprotect against market abuse.”15

As clearing was explained earlier, EMIR will lead to a reduction of credit risk (since the clearing party will bethe contract counterparty for all cleared trades), but may increase liquidity risk as larger guarantees have to beplaced at clearing houses. Since this regulation has a big impact on the energy trading market in theNetherlands we will address this regulation specifically in our control framework.

11 http://www.rijksoverheid.nl/onderwerpen/energie-en-kleinverbruikers , accessed latest 26-08-201212 http://www.rijksoverheid.nl/bestanden/documenten-en-publicaties/kamerstukken/2008/05/19/voorstel-van-wet-tot-wijziging-van-de-elektriciteitswet-1998-en-de-gaswet-ter-verbetering-van-de-werking-van-de-elektriciteits-en-gasmarkt-kamerstuknummer-31374/8058566.pdf , accessed latest 26-08-201213 http://www.iasplus.com/en/standards , accessed latest 26-08-201214 http://www.ifrs.org/IFRSs/IFRS.htm , accessed latest 26-08-201215 http://www.fsa.gov.uk/pages/about/what/international/pdf/emir.pdf , accessed latest 26-08-2012

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2.3.1.7 Legal risk

When energy trading companies trades in energy mostly contracts are used to settle the trade (Dahlgren,2003). Contracts may vary from simple futures contracts and forward rate agreements through swaps and on toincreasingly ingenious and complex contracts and even tailor-made hedges for customers (Weron, 2000).Beside the physical delivery risk described above, because of the complexity of the contracts there is also therisk that legal implications of contracts are not fully understood or cannot be enforced. This can lead todifferent interpretations of the contract of the two parties which could for example have a big impact on thenetting.

2.1.3.8 IT General Controls

The risks described above all have been defined specifically for electricity trading. Another risk for electricitytrading companies is that the information technology (IT) is not integer. If (part of the) IT is not integer, datacould not be complete / accurate anymore or applications might stop working. IT risks therefore has a impacton all other risks; e.g. operations might stop if IT stops working, there are legal requirements on the retentionof data etc.

To mitigate general IT risks, IT general controls are commonly defined in organizations based on IT controlstandards like Cobit16. IT general controls mostly cover the following areas: change management (how to makechanges to e.g. application), user access management (how to grant / monitor / revoke access rights), computeroperations (e.g. back-ups / batch schedules) and program development (e.g. development of new ITapplications).

General IT risks are as the name includes, general risks. They are applicable to all companies making use of ITand are not specific to electricity trading companies. The general IT risks will therefore not be in scope for thisthesis, in this thesis we will focus on the risks specific to electricity traders. We do however recognize theimportance of strong IT General Controls. As the overall IT environment becomes more mature, for instance bymeans of control monitoring, the design and operating effectiveness of the IT General Controls becomesincreasingly important. Moreover, investigating the possibilities of continuous control monitoring on the ITgeneral risks is a new research on itself, we encourage further research on this subject.

2.4 Internal ControlsIn paragraph 2.3.1 we have summarized the major categories of risks for electricity trading based on the theorysection. In order to mitigate risks, controls are used in organizations. As described in the paragraph 1.2, theobjective of this research is to create a framework for internal (continuous) control monitoring. Now that wehave identified the risks, we will investigate the controls that can be implemented to mitigate these risks. One ofthe most used definitions of a control in scientific research is:

“Management control can be defined as a systematic effort by business management to compare performanceto predetermined standards, plans, or objectives in order to determine whether performance is in line withthese standards and presumably in order to take any remedial action required to see that human and othercorporate resources are being used in the most effective and efficient way possible in achieving corporateobjectives” (Mockler, R.J.; 1970)

This definition describes a control as a ‘systematic effort’ to ‘compare performance’ to ‘predeterminedstandards, plans, or objectives’. Predetermined objectives are thus needed to create a control. To illustrate acontrol objective and control we will use salary payment as simple example. There is a risk of inaccuratepayments to the employees. A control objective can be that all (this is the predetermined objective) paymentsare made accurately. A control that can be defined to reach this control objective is that all outgoing paymentsare checked and authorized by an authorized employee on accuracy.

This separation of risk, control objective, and control sets the structure for our framework for internal(continuous) control monitoring. As a basis we have described risks categories in electricity trading which wecan further specify in the major risks per category. For these risks we need to describe the predetermined

16 http://www.isaca.org/Knowledge-Center/cobit/Pages/Overview.aspx , accessed latest 26-08-2012

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control objectives. Per control objective we will identify (continuous monitoring) controls that can be used as a‘systematic effort’ to ‘compare performance’ to the control objectives. The continuous control monitoringaspect as described in the objective will thus be investigated on control level, the risks and control objectives aregeneric to Electricity trading.

2.4.1 Continuous control monitoringThe focus of this thesis is on the continuous control monitoring to allow management to manage risks in a moremature way. We investigate continuous control monitoring controls to mitigate risks.

A common ambiguity in risk management is that the terms continuous control monitoring (CCM) andcontinuous auditing (CA) are used intertwined. For clarification we briefly define the difference between thetwo constructs. CCM is on the ‘control level’, describing how a control can be continuously monitored againstthe control objective. These control can be first or second line controls. CA is on a ‘risk level’, it is the auditing ifrisks are adequately mitigated, focusing primarily on the annual statement of the financial audit. In our thesiswe only focus on CCM since our framework is created primarily for the first and second line of defense.

2.4.1.1 Continuous control monitoring

Continuous control monitoring is a feedback mechanism, primarily used by management, to ensure thatsystems operate and transactions are processed as prescribed (Handscombe, 2007, p.1). Continuous controlmonitoring ensures that policies and procedures are adhered to, and that business processes are operatingeffectively. Continuous control monitoring typically involves automated continuous testing of all transactionswithin a given business process area against a suite of controls rules. Monitoring should be applied real-timeand involves active notification. The main purpose of continuous control monitoring is to provide insight and tomitigate risk by detecting irregular transactions.

As discussed, monitoring can be implemented on controls. Continuous Control Monitoring (CCM) is a feedbackmechanism to monitor the systems and operations work on controls. We have discussed the definition of acontrol in paragraph 2.4., a control is an activity used to measure against a control objective to mitigate a risk.Normally controls are always embedded in the organization, but are tested on their effectiveness periodically.Continuous control monitoring is simply stated continuously testing if the control activity was effective applied.To link this to the example illustrated in paragraph 2.4 the control was that ‘all outgoing payments are checkedand authorized by an authorized employee on accuracy’, continuous control monitoring in this case could bethat on continuous basis it is verified that this control is applied, thus continuously verifying that a payment ischecked and authorized before sending. A detective control is that management has a dashboard with alloutgoing payments and if payments were authorized. Management can thus see directly if an outgoing paymentdoes not have an authorization, for example with an alert. Please note that this control might be moreeffectively applied by using an application control (application controls are explained in the paragraph 2.4.1.2)but this is merely used as example.

An example of CCM is also described in the research of Alles et al. (2006). In this research controls that areaudited in SAP have been made CCM. Data is continuously extracted out of SAP and analysed by a number ofbusiness rules. In case rules were not met an alert was send to the auditors.

2.4.1.2 Monitoring versus application controls

CCM has a monitoring character, whether a control is continuously effectively applied. Another way to makesure a control is always effectively applied to simply force in the application that the control is followed. This iscalled an application control. Application controls are those controls that pertain to the scope of individualbusiness processes or application systems, including data edits, separation of business functions, balancing ofprocessing totals, transaction logging, and error reporting. Therefore, the objective of application controls is toensure that (Bellino and Hunt, 2007):

input data is accurate, complete, authorized, and correct; data is processed as intended in an acceptable time period;

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data stored is accurate and complete; outputs are accurate and complete; a record is maintained to track the process of data from input to storage and to the eventual output

Where application controls have a preemptive character and tend to block entries deviating from the regularpatters (hard controls), monitoring will allow the entry but will alert the user and/or a second line employee.

Because of this terminology it can be argued that application controls are not continuous controls; as statedbefore, application controls have a preemptive character and tend to block entries deviating from the regularpatters.

2.4.1.3 Continuous Control Monitoring methods

CCM is the continuously monitoring if a control is continuously effectively applied. We have identified thedifference between CCM and application controls. The remaining question is, now that we have defined CCM,how can this be applied in an organization?

There are various ways to apply CCM since monitoring can be performed in various ways. It can be argued thatby an employee monitoring the (control) activities via security camera’s is also a form of CCM. In this thesis wewill maintain a data oriented approach; we will focus on CCM applied on (Information CommunicationTechnology) data and specifically CCM in applications. This approach is approach since for CCM software isused (Harrison et al, 2009) and applications also are of major importance in electricity trading (refer toparagraph 2.2.4).

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3. AnalysisIn chapter 3 we try to define a framework for continuous control monitoring, which will be presented in chapter4. The theory of chapter 2 formed the starting point for the case studies performed. During the analysis weidentify three main phases, namely:

1. Case study at energy trading company – interviews were performed to acquire additional knowledgeconcerning the risks, control objectives and the controls itself.

2. Analysis with experts – based on the theory and case studies performed in the risk framework thatderived from the theory section, the control framework was further updated with the input from theenergy trader. The resulting framework was discussed with several industry and trading experts. Basedon the validation, the framework was additionally updated.

3. Validation of framework – after creation of the framework, first, second and third line employees ofenergy suppliers were asked to validate the framework. The validation lead to additional improvementsof the framework.

This is also depicted graphically in figure 2 in paragraph 1.5.

3.1 Case study at an energy trading companyThe input of the theory study and resulting framework were the starting point of discussion during the casestudies performed at an energy trading company. The seven risks described were discussed on continuouscontrol monitoring controls with representatives of the three relevant stakeholders to the IT audit controlframework. The case study did not contain a costs / benefit analysis, but since the main objective was to identifythe possibilities of continuous control monitoring in electronic trading, we promote further research on thecontrols identified.

Based on combining the seven risks in energy trading with the theory on risk management and continuouscontrol monitoring the following framework is created. This framework was discussed during the case studies toverify which continuous control monitoring is applicable to cover each of the risks.

Risk category Risk Risk rating Controlobjective

ContinuousMonitoringcontrols

Credit riskMarket riskLiquidity riskPhysicaldelivery riskOperational riskRegulatory riskLegal risk

A brief summary of the case studies is added below. The validation of the framework has been added underparagraph 3.2. Based on the case studies and validation of the framework the perceived added value of theframework is described under paragraph 3.3.

3.1.1 Trade managerWe have interviewed a manager of the trade department of a big integrated Dutch energy supplier. Thecompany generates energy, is a grid net operator and supplies energy to end-consumers. As described above,the ‘wet onafhankelijk netbeheer’ was rejected thus separation is not enforced anymore. The interviewedcompany stayed an integrated energy company. The trading department consist of approximately 10 traders ofwhich the majority are asset backed and proprietary traders. The organization itself is still in the process ofdeveloping a three lines of defense structure, no audit department oversees the trading activities.

Based on the discussion on continuous control monitoring in energy trading the manager informed us that acontrol oriented environment limits the freedom of traders and may have a negative impact on their primary

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concern, which is to maximize gains. As such, although proposed controls with regards to among others creditrisk and market risks are important, they are considered of the primary responsibility of the Mid-Office. Inaddition, it is noted that freedom of traders is perceived as an industry accepted risk. Traders will utilize theroom provided in an effort to gain maximal added value. The manager expects that because of the increase onregulation on credit management (like EMIR) there will be more focus on liquidity management. Energytraders do not have unlimited resources and thus have to carefully manage the liquidity outstanding (at clearinghouses).

Continuous control monitoring could according to the manager be most of value on the calculations of the VARand the Liquidity at risk (how much liquidity is outstanding). Currently these calculations are performed on daybasis per trade book. Although the manager has limits set on the VaR per trade book, he can only verify if theselimits are reached on day basis. These are two controls in which he perceives continuous control monitoring ofmost additional value. The manager assessed all other controls, including operational risk controls, off lesseradded value for his direct responsibility of managing a trade department. We note that no key controlframework is utilized by the trade manager.

An added layer of complexity are the special controls. Currently, the Enterprise Trading and Risk Management(ETRM) platforms does not facilitate the registration of special contracts. As a result, not all relevantinformation is stored in the system. When introducing continuous monitoring on for instance VaR positions, allrelevant information should be stored within a single system.

3.1.2 Risk managementWe have interviewed the risk manager of the same company as the manager of the trade apartment describedabove. The risk manager is currently the only person responsible for risk management in the tradingdepartment.

The risk manager described that his focus as risk manager is mainly on the credit risk and market risk since heperceives these risks as the risk most benefiting of a trade manager. Other risks such as operational risks weredescribed as the responsibility of the business (first line).

The risk manager also addressed the liquidity risk that will increase because of EMIR. He described that thisregulation could also be fulfilled with Commission Sharing Agreements (CSA) directly with counterpartieswhich are settled daily on their valuation (margin call). This however requires better monitoring on the VARand liquidity at risk since because of the volatility of the contracts there will also be more variation in themargin being paid / received.

Other risks that were addressed by the trade manager based on the control framework is the credit limits percounterparty. Currently these limits are already in place, however the brokers only show anonymous bids. Atthe end of the day the brokers give insight in the credit limits of the energy trade company per counterparty.Having continuous insight in these limits and performing pre-deal analysis would be of added value. The trademanager notes that the current IT environment is not suitable for continuous monitoring controls, as certaincalculations may take several hours.

For supply chain traders and asset backed traders, although not directly linked to electricity trading, the actualproduction of energy trading should be monitored as the largest financial risks are perceived in un-ability toproduce. Production of electricity is the core competence of an supply chain trader and the majority of tradesare a consequence of the production cycle. Although credit and market risks are the largest risks directly linkedto trading, physical delivery should be included in the framework as well. Based on these discussions,performance risk was split up into two sub risks, namely own generation and counterparties generation. Therisk manager notes that counterparty generation is primarily the risk of the counterparty, but can have an effecton the companies result via credit risk, market risk and/or operational risk.

A remark is made regarding dependency on brokers and clearing houses. As systems of external parties areused for trading, these systems should facilitate additional monitoring actions in order to implement allrelevant controls such as frequent/continuous updates of the counterparty white list and maintaining traderlimits.

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3.2 Analysis with expertsAs described above the theory and orientation led to an initial framework analysis which was discussed in twocase studies. To improve the quality of the framework and to verify the completeness and accuracy of theframework, several experts of a big four accountancy firm validated the model. Among others, several energytrading experts and financial trading experts with an EDP-Audit background evaluated the model.

Energy trading experts

During our interviews with the energy trading experts with an IT-audit background, the primary focus on thedegree of continuous control monitoring proposed and the nature of the defined controls. Controls werecategorized by means of the Gartner (2011) model, but this proved to raise more questions than it answered. Asthe control itself was clear, but discussions started about which type of continuous monitoring control categorywas applicable, we decided to remove the categorization from the framework itself. As ultimately the way acontrol is designed and/or implemented determines how a control is perceived, a large percentage of controlscould be placed into various continuous control monitoring categories. In addition, controls can consist ofmultiple types of continuous controls as identified by Gartner (2011). We therefore note that a more hands onapproach of classification of continuous controls would benefit the discussion. We encourage others to followup on these discussions.

In addition, the following topics were actively discussed:

In order to implement continuous monitoring controls, all relevant information should be completeand accurately available in the ETRM. At current not all trades are confirmed within the energy sectorand even when trades are confirmed, ETRM platforms do not always support all types of trades (as alsonoted by the trading manager in paragraph 3.1). As such, questions can be raised regarding thereadiness of the industry regarding continuous control monitoring. This is discussed in more detail inparagraph 3.4 and chapter 6.

The applicability of proposed controls was discussed for both spot and future market. As a result, anadditional column was added to the framework showing applicability for both.

Although the risk of paying unbalance prices due to failure to deliver by counterparties is the risk of theparty that did not deliver as agreed upon, without internal controls addressing this risk and being ablethat the counterparty should pay the unbalance price, the buyer is made responsible. Therefore,physical delivery cannot be scoped out completely. This is in line with the discussion the risk managerfrom the case study.

Upcoming legislation such as EMIR sets demands for central clearing and reporting of trades. To makethe framework more robust for future references, controls are added to for upcoming EMIR legislationobligations.

A discussion was held regarding the importance of data quality and the way this can be continuouslytested. Sufficient data quality and thus Data Management is required for effective forecasting and pre-deal analysis models. An effective method of testing data quality is back testing. Back testing can beimplemented in such as way that the quality of forecasting is continuously tested by means of backtesting. A control is added to the framework.

Financial trading expert

As legislation and trends in the energy trading industry follow financial trading, the framework was discussedwith a financial trading expert. The discussions were held with a subject matter expert with an RE background.The following discussions were held:

The importance of liquidity for electricity traders was discussed. As a result, liquidity risk is includedas a separate risk, whereas first liquidity risk was defined within credit and market risk. EMIRlegislation requires usage of central clearing parties for OTC trades, in which the clearing party carries

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the credit risk of failure to pay by the counterparties. To manage this risk, clearing parties requiredeposits and/or guarantees by the trading companies. These guarantees effectively reducing theliquidity of a trading company and effectively heighten the liquidity risk. Following trends in financialtrading, liquidity risk is listed as a separate risk in our framework.

The Value of Return model is based on a normal distribution, which provides certainty under normalmarket conditions. A big risk in trading is however the tail risk. This tail refers to the outliers of thebell-shaped distribution curves, which show statistical probabilities of a variety of outcomes. Tail riskrefers to low chance / high impact situations such as geographic instability, natural disasters or thecollapse of financial markets. Managing tail risk is therefore added to the framework. We do howeverhave remarks concerning the applicability of continuous control monitoring, as awareness, experienceand scenario analysis will likely provide best mitigation of the risk involved. Tail risks can however bepartly mitigated by limiting dependency to counterparties or regions.

Completeness and accuracy of registration and confirmation of trades is primary an operational riskand should be listed as such. To avoid duplicates in the framework, completeness and accuracy ofregistration is removed from credit risk.

General Ledger accounting is currently not in scope. This should be made more explicit. The limitationwas added to paragraph 1.4.

3.3 Validation of frameworkThe constructed framework was submitted for validation to the first, second and third line of defense of energytrading companies. During follow up sessions, the comments raised were discussed. The following paragraphsprovide an overview of the comments noted. First, the comments of a joint interview with the first and secondline managementare discussed. Afterwards, the comments of the head of the external audit department ofanother energy trading company are mentioned.

3.3.1 Validation at line management and risk management of an energytrading companyThe proposed framework was validated by the head of the trade department (first line) and the risk manager(second line). We note that the three lines of defense model is not completely embedded at the company androles within the Back-Office differ slightly from the proposed framework, some Back-Office tasks such as staticdata maintenance for thresholds is performed by the second line. Five main critiques were the starting point forthe discussions held. The following paragraphs describe these critiques and following discussions:

Firstly, Physical delivery risk is not restricted to trades, as this is not measured on a trade level but on aportfolio level. If situations with unbalances occur, additional trading is not the initial response. Being an assetbacked trader, both boosting the production of power plants (increasing supply) and asking clients to decreaseconsumption (decrease demand) are options. In our opinion the comment stresses the added value ofcontinuous monitoring controls, as (customer) demand, supply and own generation cannot be managedseparately, but the whole portfolio has t0 be managed in order to optimally match supply and demand at mostbeneficiary terms. If not automated and continuously monitored, the likelihood increases that unbalancesituations are not timely detected and suboptimal terms have to be accepted.

Secondly, the described VaR analyses are of a higher maturity than the organizations (or industries) current ITenvironment. The current calculation time of the VaR is two hours. Therefore, although the added value isconfirmed, continuously updating the VaR or pre-deal analysis is not considered feasible without technologicalimprovements and/or different approaches to calculating the VaR. We note that the maturity of both the ITGeneral Controls as well as the IT environment itself pose challenges for early adoption of the framework, butare considered issues that can be resolved and do therefore not significantly impact the framework, only thethroughput time at which (parts of) the framework can be implemented.

Thirdly, the columns with the roles are not always filled to the expected extent, as follow up of alerts is notalways defined. The actions of the various departments do not stop at receiving an alert, active follow up isrequired. Examples are monitoring activities of Mid-Office, such as credit risk. The comment underlines the

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scoping of the framework and provides topics for follow up of the work performed to date. We note howeverthat follow up of the identified risk is not the primary focus of the framework. The framework is constructed totimely detect risks and assign these risks to the appropriate department. We encourage others to follow up thecurrent framework by further describing the continuous controls and follow up of the actions.

Fourthly, a comment was raised about the information available on counterparties for exchange trades. Asdiscussed in paragraph 2.2.2, counterparties are not always showed by exchanges. Implementing continuouscontrols on for example white lists of counterparties implies that the exchange must facilitate this. Due to thedependency, the control will be more difficult to implement. Regarding counterparty listings, we take want toput a slightly different approach up for discussions. As a starting point, a deal should always be made taken intoaccount all pro’s and con’s. A management decision regarding performing a trade above a certain counterpartyor financial threshold should always be possible, as long as an educated decision is made by appropriate levelmanagement, for instance a board decision. Key elements are transparency and involvement of all relevantparties involved. Therefore, instead of limiting the trade information to only deals that confirm to white listthresholds, other relevant deals must also be visible, as long as it is clearly marked as not white listed. Inaddition, other deals may provide valuable information for market risk forecasting models and may exposemarket trends.

Fifthly, the VaR on interest rates and FX rates were considered a very strong control for the perceived riskinvolved. The risk level were perceived lower as trading is frequently limited to a certain geographic region,which limits interest and FX rates volatility. We note that when not limited to electricity on grid, but includingtrades on raw materials, the geographic spread is increased and the relevancy of an FX VaR increases. As aresult, the risk level of interest VaR is lowered to low and the risk level of FX VaR is lowered to medium.

3.3.2 Validation at internal auditor of an energy trading companyWe inquired the (former) head of the Internal Audit department of a large electricity supplier and trader. Fortransparency reasons, we note that the inquiry was performed two months after the employment of the head ofthe audit department ended. We note that the interviewee has a strong IT-Audit background.

The manager Internal Audit could not emphasize the importance of controls regarding credit risk enough.When performing credit risk analyses, the ultimate beneficiary party of a trade should be identified to mitigaterisk. In addition, the importance of appropriate authorization management controls was emphasized, asexperience thought the manager that frequently the rights are not in line with the required situation. Dependingon the action implementation, this risk should be adequately addressed by operation risk controls currentlydescribed in the model.

4 main critiques were added. First, the framework is not structured to show the largest risks first. As physicaldelivery risk may be the largest risk for a power generator that also trades, this is not the largest risk based onthe scope of the thesis, namely electricity trading. The risks should thus be reorganized in a way that is morerepresentative of the risks noted with relation to your research question. As a result, the order was adjusted tocredit risk, market risk, liquidity risk, operational risk, physical delivery risk, regulatory risk and legal (contract)risk. We note that a consensus exists between interviewees that the first four risks are most important, althoughthe position of operational risk can be debated. We note that credit risk, market risk, liquidity risk, operationalrisk and physical delivery risk all have a high inherent risk rating.

Second, the focus of market risk is primarily focused on long term market risk. In his experience as head of theaudit department, short term volatility is an important part of market risk. Weather conditions for instance canhave a large effect on demand of electricity. A few degrees lower will spike energy consumption by means ofheating, while a few degrees higher significantly increases energy consumption due to additional airconditioning. Short term volatility should thus be added to the market risk and has potential for automationand continuous control monitoring. Automated forecasting models based on continuous back testing may provea valuable add-on to energy trading companies control environment. In line with the comments noted by thetrade manager and risk manager, forecasting and matching supply and demand is an important part of trading,as demand determines supply and demand is volatile. Forecasting of actual short-term demand is thereforeadded to the framework as part of market risk.

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The third critique is that behavior of traders is understated in the current control framework. In general,traders act in line with patterns that are most beneficial to them, thus which provides them the highest bonus.This leads high risk behavior and should thus be monitored. A reward program should be designed that doesnot reward high risks and automated monitoring against the policy can be created. This is in line with thestatement regarding maximizing gain of the trade manager during the case study. As a result, an additionalcontrol was added to the framework regarded measurement of trader risk appetite and adherence to tradingpolicies.

Fourth, impact of political decisions such as the decision of Germany to shut down nuclear power plants has ahuge impact on the energy market. These political decisions are not explicitly mentioned under market risk. Wenote that we consider significant political actions as a tail risk: a small likelihood, big impact event. A commentwas added in the framework.

In addition, a comment was raised regarding the Market Risk control of a VaR for FX rates. As the scope waslimited to electricity on the grid in the Netherlands, this risk is not applicable. This however is a risk whendealing in commodities or when trading in other countries. For the scope of the thesis, the risk was removed.

3.4 Perceived added value of the frameworkThe main objective of this thesis is to come up with an internal continuous control monitoring framework forelectricity trading. Based on the case studies and validation a framework has been created which is included inchapter 4. Having a good control framework does however not automatically mean that you are in control. Notonly should there be a good design of controls, controls should also be operating effectively in the organization.During the case studies and feedback we asked all participants on their perceived added value of the frameworkto get a feeling if the framework has a chance to be operating effectively in an electricity trading organization.

All inherent risk levels were evaluated during the expert interviews and validations. The credit risk, market riskand liquidity risk were deemed as the highest risks affiliated with electricity trading. The overall risk ofperformance risk was recognized by all involved parties as being an important risk for companies that supplyelectricity, but is less directly linked to trading itself.

Based on the discussions about the control framework we note that currently most added value of continuouscontrol monitoring is perceived on the calculations of the VAR and liquidity at risks. In addition, more frequentupdates of counterparty white lists, trader limits and other transaction screening are considered relevantimprovements. The affiliated credit risk, market risk and liquidity risks are also identified as being the largest,as these are risks that energy companies face on daily basis and can have a great impact on the results oforganization. However, although the control is continuous, these are currently considered detective controls. Ifnot enough preventive applications controls are in place, the risk exposure already occurred before identified.As such, corrective actions have to be performed. Only when for instance pre-deal VaR calculations areperformed and additional approval is needed to continue with deals that are not within predefined thresholds,will the risk preventatively be managed.

In addition, a more sophisticated method of forecasting, including continuous back testing of forecastingmodels, is welcomed as a valuable addition to the current control environment. As with pre-deal VaR, thiscontrol requires a high level of maturity.

Last, a continuous approach in validating access rights and monitoring actions of traders is welcomed. Theindustry experts and third line emphasize that although granting, removing and monitoring access rightsshould be a relatively straight forward process, the current designs are error prone. A continuous monitoringsolution would provide added value and limit operational (fraud) risk opportunities.

For additional consideration, we refer to chapter 6.

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4. The frameworkThe following chapter describes the continuous control framework as created and validated in the previouschapters. First, the structure of the framework is explained. Second, the framework itself is presented. Last,implications for the IT Audit is discussed.

4.1 Structure of the frameworkFor all of the seven main risks defined, the following is determined:

Control reference – C(redit), M(arket), L(iquidity), O(perational), P(erformance), R(egulatory) andL(egal) risks can be distinguished by their control reference

Risk

Inherent risk rating

Control objective

Nature of control: Preventative / corrective / detective

Degree of automation of control: Manual / IT dependent / Automated

Derivate impacted by the control: Spot / Future

Trading type impacted by the control: Over the Counter / Exchange

Can the control be continuously performed: Yes / No

Description of the Continuous monitoring control

Roles and responsibilities of Front-Office, Mid-Office, Back-Office and Second line

Additional comments

Please note that the trader type has not been included in the framework. As described in paragraph 2.2.1proprietary traders are not in scope of this thesis. The difference between asset backed and supply chain tradersis that asset backed traders have more inherent risk. The whole risk rating of the framework would thus behigher for asset backed traders, no individual risk ratings would be different.

Additionally the software type (e.g. ETRM software or Excel) as described in paragraph 2.2.4 is not included inthis framework. This is not included since most controls can be implemented in more software types.Dashboards could for example be included in an ETRM package, but also in an excel sheet using continuousdata extraction. Since no ambiguous categorization can be made on the controls, software types have beenexcluded from the framework.

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4.2 The framework

Ref Risk InherentRiskrating

Controlobjective

Preventative /corrective/detective

Manual / ITdependent/automated

OTC /Exchange

Spot /Futuremarket

Continuous

CMM control Front-Office

Mid-Office Back-Office

2nd line Comments

C1 Counterpartiesfail to pay asagreed in thedeal.

High A credit check/ analysis isperformed onallcounterparties.

Preventative

ITdependent

OTC Spot &Futuremarket

Yes Trades can only be performedwith counterparties on a whitelist for which a credit check hasbeen performed (applicationcontrol). Tolerance limitsdetermining how much is atmaximum allowed to be tradedwith the counterparty areincluded. Tolerance limits iscovered in control C3.

This white list is continuouslybeing monitored on its relevance(are a large number of tradersfor example bounced because ofthe white list) and the SoD(traders cannot maintain / entercounterparty (exposure)information).

Counterpartyratings arecontinuouslyupdated basedon third-partyinformationand internalriskprocedures.

In case a largenumber ofdeals arebounced witha counterpartybecause of themaximumexposure,Mid-Officereviews if theexposure isstill valid.

The backofficeverifies bythecontinuousSoD checkthat onlytraders cantrade andonly themiddleoffice canenter /maintaincounterparty(exposure)information.

RiskManagement can beconsultedby Mid-Office forqueriesregardingthe whitelist.

This control is forOTC trades only incase the deal is notcentrally clearedyet (then the creditrisk is limited tothe clearinghouse).

We note that inthis scenario, dealsthat do not meetfull requirementsare shown.

C2 Counterpartiesfail to pay asagreed in thedeal.

High Trades areonlyperformed inline with themaximumexposuretolerance asincluded onthe creditcheck /analysis whitelist.

Preventative

ITdependent

OTC &Excha

nge

Spot &Futuremarket

Yes Before accepting a transaction,the application verifies if thetrade is in line with themaximum exposure of thecounterparty. If the trade is notin line with the exposuretolerance limit (applicationcontrol).

This application control iscontinuously monitored, if apreset number of deals are

Mid-Officemaintainswhite list andreceivesinformationon the settolerancelimits forexample bythe amount ofdeals that arebounced.

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bounced within a set because ofthe maximum counterpartyexposure, an alert is generatedfor the middle office.

Using thisinformation itcan discuss settolerancelimits with thefirst line

C3 Counterpartiesfail to pay asagreed in thedeal.

Med Collateralmanagement -Creditexposure isappropriatelymanaged.

Preventative

Automated

OTC &Excha

nge

Spot &Futuremarket

No Credit exposure is automaticallycalculated. Mid-Office is alertedwhen credit exposure exceedspredefined thresholds.

Mid-Officemonitorscreditexposure ofcollateralmanagement.When needed,tradingrestrictionsaredetermined.

C4 Counterpartiesfail to pay asagreed in thedeal.

Med Trades areonly clearedby reliableclearinghouses.

Preventative

Manual

Exchange

Spot &Futuremarket

No Traders only have access toapproved clearing houses.Utilised clearing houses aremonitored by Mid-Office.Whenusing clearing houses, thecounterparty credit risk iscovered by the clearing house.This control is the same ascontrol C1, but then related tothe clearing house since theclearing house is the financialcounterparty when a deal iscleared.

Mid-Officedetermines incooperationwith RiskManagementwhich clearinghouses may beutilised.Access fortraders is onlyprovided tothose clearinghouses.

RiskManagement may beconsultedby Mid-Officewhenselectingclearinghouses.

As exchanges takeover theresponsibility ofpayment ofcounterparties ,the exchange itselfhas to be solventenough to not posea credit risk. Thisrisk is limited dueto the AFMregulations onclearing houses.

C5 Credit riskgovernanceand reportingrequirements

Med The companyadheres togovernanceand reportingrequirements.

Preventative

ITdependent

N/A N/A Yes A online real-time dashboard isavailable on the creditinformation. Preset rules in theapplication are created on thecredit requirements.

When these rules are metautomatic reports are created bythe application, to fulfilgovernance requirements.

Mid-Officemonitors theICAAP creditdashboard andreceives theautomaticgeneratedreports.

In case creditrequirementsare not metMid-Officeprovidesfollow-up tomeet therequirements.

This concerns aregulatory riskrelated to creditrisk.

C6 Insufficientcounterpartyspread leads to

Med Individuallimits are seton the amount

Detective

ITdependent

OTC &Excha

nge

Spot &Futuremarket

Yes Predefined combinations areonline real-time visibleanalyzed. Custom combinations

Mid-Office isautomaticallyalerted when a

RiskManagement

Customcombinations canbe created per

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highcounterpartydependency

of trading withcounterpartiescovered incontrol C3.

Additionallyanalysis areperformed oncombinationsofcounterparties, trade types,etc.

analysis can be performedwithin the application.

predefinedcombinationsis triggered.The alert isinvestigatedand followed-up by Mid-Office.

periodically performsa qualitiveanalysison thevariouspossiblecombinations.

trader, workbook,region, energytype, etc.

C7 In case EMIRwill beimplementedthere is a riskthat trades arenot cleared bya centralclearing housewhen requiredby EMIR (forOTC trades).This risk is aregulatory riskand covered inR5.

Med N/A refer torisk

N/Arefer

to risk

N/Arefer

to risk

N/Arefer

to risk

N/Arefer to

risk

N/Arefer

to risk

N/A refer to risk N/Arefer torisk

N/A refer torisk

N/A refer torisk

N/A referto risk

The requirementsof EMIR are notfinal yet.

M1 The value ofrisk due topricefluctuations isnot in line withcompanies' riskappetite.

High A Value atRisk model ismonitored toprovideinsight intocurrent riskexposure.

Preventative

Automated

N/A N/A Yes Online real-time visiblecalculation of Value at risk pertrader and per tradebook/timeslot. In case presetlimits are crossed notificationsare send to the trade manager tobuy / sell positions to reduce theVaR.

Thetrademanagerreceivesnotifications incaselimitsarecrossed.

Mid-Officemonitors theVaRdashboard andsets the limitsfor thenotifications.

M2The value ofrisk due topricefluctuations isnot in line withcompanies' riskappetite.

Med The VaR isappropriatelymanaged,forecastingcalculationsare performedon the VaRbefore a tradeis settled.

Preventative

Automated

OTC &Excha

nge

Spot &Futuremarket

Yes Based on the online real timedashboard described in M1,traders get a notification in casethey want to perform a tradewhich would make the VaR crossa present limit.

Tradersarelimitedin theirtradesby theVaR.

Mid-Officemonitors theVaRdashboard andsets the limitsfor thenotifications

M3 The risk ofunexpectedchanges of a

High The effect ofthe volatilityon the

Preventative

ITdependent

N/A N/A Yes Online real-time visiblecalculation of effect on volatilityon the portfolio depicted in a

Thetrademanager

Mid-Officemonitors thevega

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portfolio valueas a result ofchanges in thevolatility ofmarket

portfolio iscontinuouslycalculated(vega)

real-time dashboard. In casepreset limits are crossednotifications are send to thetrade manager to buy / sellpositions to react on thevolatility predictions.

receivesnotifications incaselimitsarecrossed.

dashboard andsets the limitsfor thenotifications.

M4 The risk ofshort termchanges indemand due toknown demandindicators isadequatelyforecasted.

High Short termdemandforecasting iscontinuouslyupdated.

Preventative

Automated

N/A N/A Yes The expected short term demandis continuously forecasted basedon latest weather and socialinformation available.

Forecasting is based on acontinuous self learningforecasting tool.

Thetrademanagerreceivesnotifications incaseshorttermforecasteddemandvariesfromshorttermsupply.

Known influencersof demand are forinstance theweatherconditions,holiday and socialevents

M5 Tail risk andbig events arenot included inmarket riskmodels

Med Tail risks areeffectivelymanaged.

Preventative

ITdependent

N/A N/A Yes Using data forecasting services,the tail risk of possible bigevents is calculated.

See C5 foroperationalmonitoring ofpossible tailrisks by Mid-Office.

Based ontheforecasting, RiskManagementdetermines whethercurrentpositionsdo notpose a toohigh riskfor thefuture.

See for instancerecordedfuture.com for forecastingservices.

Note thatunforeseen policaldecisions are alsoconsidered tailrisks.

M6 The VaR isinaccurate

Med Datamanagementprocedures arein place toverify theaccuracy ofVaR.

Detective

Automated

N/A N/A Yes The accuracy of the VaR iscontinuously tested by means ofback testing with the most up-to-date historic data.

In case the VaR calculationsdiffer x amount of the actualvalue or other VaR calculationmethods are tested as moreeffective a message is send toMid-office.

Follow up ofmessages onthe backtesting of theVaR method,analysis if thecurrent VaRcalculationmethod is themost effectivemethod.

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M7 Fluctuations ofmarket pricesof interestrates

Low A Value atRisk model ismonitored toprovideinsight intocurrent riskexposure.

Preventative

ITdependent

N/A N/A Yes Continuous calculation of Valueat risk by continuously updatingthe interest rates information.For the VaR dashboard refer tocontrol M2.

Tradersarelimitedin theirtradesby theVaR.

Mid-Officemonitors theVaRdashboard andsets the limitsfor thenotifications.

Trading companyto decide whethera VaR is necessary.

LIQ1

Risk of havingto sell otherassets atuneconomicrates to fundupcomingtrades or otherobligations.

(Liquidityfunding risk)

High Liquidity atrisk (LAR) isappropriatelymanaged,forecastingcalculationsare performed.

Preventative

ITdependent

N/A N/A Yes Liquidity at risk is continuouslycalculated visible in an onlinereal-time dashboard.

Continuous update of theunderlying market data for thecash flow forecasting.

This is online made visible in areal-time dashboard of the LaRin which set tolerance limits cangenerate alerts.

Theteamlead oftheFront-Officemonitors thedashboardproactively andcanrespondto alertswhengenerated.

Mid-Officeresponds toundesirableLAR positionsand setstolerancelimits.

Liquidity at risk isthe opposite ofpotential futureexposure.

It is veryimportant for thisrisk how the LaRis calculated,which is coveredby LIQ5

LIQ2

The LaR isinaccurate

High Datamanagementprocedures arein place toverify theaccuracy ofLaR.

Detective

Automated

N/A N/A Yes The accuracy of the LaR iscontinuously tested by means ofback testing.

In case the LaR calculationsdiffer x amount of the actualvalue or other LaR calculationmethods are tested as moreeffective a message is send toMid-office.

Follow up ofmessages onthe backtesting of theLaR method,analysis if thecurrent LaRcalculationmethod is themost effectivemethod.

LIQ3

Collateral(management)is notappropriatelymanaged.

Med All the margincalls areassessed onaccuracy.

Preventative

ITdependent

OTC &Excha

nge

Spot &Futuremarket

Yes The system automaticallyperforms a plausibility analysison received margin calls, usingthe positions registered in thesystem. When the margin callsdiffers more than x % from theexpected value, an alert is sendto Mid-Office and RiskManagement.

Mid-Officeinvestigatesthe alert.

RiskManagementmonitorstimelyfollow upof the alertandperformsqualityreviews.

For clearing,margin accountingpositions can beupdatedcontinuously.However, as acounterparty isinvolved, actualreal-time marginaccounting is notfeasible andprovides a higherrisk.

LIQ Med The trading Preven IT OTC & Spot & No The system actively reports Whenever the We note that LIQ2

34

4 companycomplies tomargin calls ofthe clearingparty.

tative dependent

Exchange

Futuremarket

predefined parties when anautomated margin call isreceived.

margin call ofa clearer isupdated, theMid-Officereceives analert an actsaccordingly.

is taken intoaccount.

LIQ5

Med All margincalls are sendto thecounterpartieswhen needed.

Detective

Automated

OTC &Excha

nge

Spot &Futuremarket

Yes The system actively monitors forall counterparties with margincall agreements if a margin callhas been send to the counterparty as agreed.

In case no margin call has beensend while this had been agreedin the deal information or isexpected by the application,Mid-Office receives an alert.

Mid-Officeinvestigatesthe alert andtriggers Back-Office to sendan margin callif needed.

Whenneeded,Back-Office(re)sendsmargin calls

O1 Complete andaccurateregistration oftrades

High All trades areconfirmedusingconfirmationstandards andregistered onlyonce.

Corrective

ITdependent

OTC &Excha

nge

Spot &Futuremarket

Partly The application monitorscontinuously if every dealtransaction is confirmed by thedealer and back office within aset tolerance limit. Whenever adeal is registered, the systemchecks if the deal is already inthe system.

In case no response is receivedan automatic message asking forconfirmation is send to thecongestion dealer / back office.

Back-Officeinquires atFront/Midofficewhenever atrade is notconfirmed.

Riskmonitorswhetherallappropriateapprovalshave beenset. A real-time alertis sendwhenevertraderegistration is not inline withpredeterminedthresholds.

Not all trades arecurrentlyconfirmed yet.

We recommendusing standardssuch as ISDA forconfirmation oftrades.

O2 The rewardsystem fortradersencourages risktaking

High Traders riskappetite isappropriatelymanaged andmonitored

Detective

ITdependent

OTC &Excha

nge

Spot &Futuremarket

Partly

O3 Unauthorizedactivities

High A SoD is inplace betweenfront-, mid-and backoffice.

Detective

Automated

N/A N/A Partly The application continuouslycompares IST and SOLLpositions and monitors forconflicting rights. Alerts aregenerated in case SoD inbreached to the second line.

Monitoring that nousers havemultipleroles /conflictingrights and

35

maintaining the SoDmatrix.

O4 Med Only approvedemployeeshave access tothe tradingportfolios,regions, typesetc.

Detective

ITdependent

N/A N/A Partly The trade manager has insightinto a dashboard containingcurrent days active traders, lastdeal made and last login timelinked to HR information(holidays, out of service).

Thetrademanagersproactivelymonitors adashboardincluding allactivetraderslinked totheir HRinformation.

SoDbreachesarecontinuously alertedto riskmanagement.

A fine-grainedaccess matrix is inplace. Access hasto be defined perportfolio, region,period, group, etc.

O5 Med Trades aremonitored onunexpectedactivities offront-, mid-and backoffice.

Detective

Automated

OTC &Excha

nge

Spot &Futuremarket

Yes Continuous pattern analysis is inplace.

Predefinedunusualpatternsfor thetrade cyclearedetermined . RiskManagementreceivesalertswhenthese haveappearedfor furtherresearch.

Continuousanalysis ontrade/back officeusers for unusualpatterns/userswhich arepredefined. Alertsare send to thesecond line in caseunusual patternsappear, e.g. Doesthe same person /IP address alwaysclear trades of aspecific trader?

O6 Forecastingmodels areinaccurate

Med Theeffectivenessof forecastingmodels (e..g.volatility risk /demandforecasting) iscontinuouslyvalidated.

Detective

Automated

N/A N/A Yes Continuous improvement offorecasting models based onhistorical information.

Back testing is implemented(model risk, as forecasting is amodel based on historic marketdata). The model continuouslylearns based on the continuousinputted data.

The Mid-Office receivesa notificationin case otherforecastingmodels appearto be bettersuitable basedon thecontinuousback-testing.

RiskManagementperformsbacktesting oncredit risk,VaR,liquidityrisk, etc.

O7 Risk appetite Med Trade Detect IT OTC & Spot & Partly The manager has access to The Second Only applicable for

36

of trader not inline with riskappetite ofcompany

behavior oftraders ismonitored onactivitiesaffiliated withincreasedrisks.

ive dependent

Exchange

Futuremarket

graphs detailing thenumber/amount of continuoustrades per trader in comparisonto other traders. In case presetlimits (e.g. a large amount oftrades in a short period) arecrossed notifications are send tothe trade manager. Analysis iscustomizable, but includes atleast trades per trader, period,portfolio, region, currency type,etc.

trademanagerhasaccess toreal-timegraphswith theexposures percounterparty andreceivesnotificationindicating oddtradebehaviour.

line isconsultedby tradingmanager.

asset backed andproprietarytraders.

O8 Trades are nottimelyvalidated

Med All trades aretimelyvalidated.

Detective

Automated

OTC &Excha

nge

Spot &Futuremarket

Yes Analysis on throughput timebetween capture and validationof trade. Unusual patterns areincluded in the control O7

O9 Trades are notin line withcompanypolicy

Med A trade policyis defined.Trades aremonitoredagainst thetrading policy.

Detective

Automated

OTC &Excha

nge

Spot &Futuremarket

Yes All trades are monitored againstthe trading policy when settled(this could be an applicationcontrol, depending on thepolicies).

The system sends an alertwhenever the trading policy hasnot been updated for apredefined period.

This control further is coveredby the LaR (control L2)and VaR(control M3).

Mid-Officeprovidesfollow-up onthe alertsreceived incase thetrading policyhas not beenupdated for apredefinedperiod.

O10 Simultaneoustrades on sameperiod/region

Low Simultaneoustrading isidentifiedbefore a dealoccurs(PortfolioManagement).

Preventative

ITdependent

OTC &Excha

nge

Spot &Futuremarket

No The application provides an alertto both traders when the sametrade period is opened. Nocontinuous control is feasible onthis application control.

Tradersandtradingmanagerarenotified.

P1 The actualdelivery andsupply ofelectricity isnot in line with

High Timelydetection ofunbalancesand follow-up.

Corrective

Automated

N/A N/A Yes On a continuous basis, theapplication matches all deliveryand supply of electricity with theforecasted delivery and supply.

Whenever analert istriggered, the

The risk owner isin most cases thepower generatoror large suppliers.

37

the forecasteddelivery andsupply.

Due toover/undercapacity, thereis a risk ofunbalance onthe grid, forwhich thecompany hasto payunbalanceprices to the(external)congestionparties.

Delivery andsupply ismonitored onthe actualversus theforecastedsupply anddemand.

Unbalancesare timelyidentified andfollowed-up ifpossible byincreasing ordecreasing thesupply ofenergy (thiscan be done bytrading).

Differences are identified andalerted to the first line ofdefense.

supplyshouldbeincreased ordecreased. Forexamplethis canbe donebytradershavingto makeadditional tradestocorrectthepositions.

P2 Companiesassets do notproduceprognosedamount ofelectricity(Internalperformancerisk).

There is a riskthat thecompanycannot produceelectricity andthus revenuesare missed.

The risk ofunbalanceprices (andother prices) iscovered in P1.

High Delivery ismonitored onthe actualversus theforecastedsupply. Timelydetection ofunbalancesand follow-up.

Monitoringcontinuously ifthe assetssupply asexpected andin case ofdifferencesfinding thecause and(makingsomeone)repairing theassets as soonas possible

Corrective

Automated

N/A N/A Yes On a continuous basis, theapplication matches all deliveryof electricity with the forecasteddelivery.

Differences are identified andalerted to the first line ofdefence.

Whenever analert istriggered, aninvestigation isstartedwhy thedifferences existsand howthis canberepaired.

It can bethattradershave tomakeadditional tradestocorrectthepositions.

Only applicable forsupply chaintraders and assetbacked traders.

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P3 Externalcompaniesassets do notproduce theamount ofelectricity thatwas agreed onin a trade.

There is a riskthat ofunbalance onthe grid. Thisrisk ofunbalanceprices based onthe deal is forthe externalcompany sincethey agreed todeliver theamount ofelectricity.

For a traderthere isadditionally arisk that bothtraders havedifferent dealinformation.This is coveredin O1 -complete andaccurateregistration ofall trades.

High N/A refer torisk

N/Arefer

to risk

N/Arefer

to risk

N/Arefer

to risk

N/Arefer to

risk

N/Arefer

to risk

N/A refer to risk N/Arefer torisk

N/A refer torisk

N/A refer torisk

N/A referto risk

N/A refer to risk

R1 Non-compliancywithregulations

Med At all times,the companyis incompliancewith itsconcerningregulationsregulations.Laws areperiodicallyreviewed.

Preventative

ITdependent

N/A N/A No Companies are informed by athird party whenever relevantchanges occur.

Legal isultimatelyresponsible.

R2 Low There iscompliancy

Corrective

Automated

N/A N/A No Information is automaticallyarchived/deleted after the

39

withregulations.Monitoring isperformed onretentionrequirements.

retention period is finished.CCM is not feasible, onlyindirect on capacitymanagement.

R3 Med There iscompliancywithregulations.Portfoliomanagementis performed -percentagegreenelectricity vs.greyelectricity.

Preventative

Automated

N/A N/A Partly There is an automated matchbetween purchased and requiredcertificates of green electricity

Tolerance limits are set whenemails are send to the trademanager (Front-Office) to buy /sell green electricity rights.

Thetrademanagerreceivesnotificationswhen tobuy/sellgreenenergyrights.

R4 Theorganizationalstructure doesnot complywith tradingrequirements

Low The three linesof defensemodel isembedded intheorganization.

Detective

ITdependent

N/A N/A Partly Continuously transactions basedon percentage or outliers arepresented to the second line assamples to be tested.

ORMperformsauditsamples.

R5 Non-compliancewith EMIR

Med Companyreports in linewith EMIRReportingrequirements.

Preventative

Automated

N/A N/A Yes The application monitorscontinuously if every dealtransaction is cleared through acentral counterparty within aset tolerance limit.

In case no response is receivedan automatic message asking forclearing is send to the centralcounterparty.

Cleared trades are automaticallyreported to Trade repositories islisted as Ref C6.

ORMreceivesalertswheneverautomatedscanningdetects anOTC tradethat havenot beenclearedcentrally.

Central clearing ofOTC trades couldbe madeobligatory underEMIR.

R6 Non-compliancewith CarbonEmissionregulation

Med CarbonEmissionrights arepurchased inline withrequirements.

Preventative

Automated

N/A N/A Partly There is an automated matchbetween purchased and requiredcertificates of CO2 rights.

Tolerance limits are set whenemails are send to the trademanager (Front-Office) to buy /sell CO2 rights.

Thetrademanagerreceivesnotificationswhen tobuy/sellCO2rights.

R7 Transparencydisclosure

Low (Future)Disclosure and

to bedeter

to bedeter

N/A N/A No To be filled in depending onfuture legislation.

EMIRtransparency

40

transparencyrequirements.

mined mined requirements.

R8 Non-compliancewith statementof origins

Low Trading withcounterpartiesprohibited byStatement oforigins isprevented.

Preventative

Automated

OTC &Excha

nge

Spot &Futuremarket

Yes Pre-transaction verification iscovered by control C2.

The desiredinformation needsto be in theapplication.

R9 Non-compliancewith NOXrights

Low NOX rightsare purchasedin line withrequirements.

Preventative

Automated

N/A N/A Partly There is an automated matchbetween purchased and requiredcertificates of NOX rights.

Tolerance limits are set whenemails are send to the trademanager (Front-Office) to buy /sell NOX rights.

Thetrademanagerreceivesnotificationswhen tobuy/sellNOXrights.

The desiredinformation needsto be in theapplication.

We note upcomingchanges in NOXlegislation.

L1 Non-compliancywith thebindingcontractualrequirements

Med Thecontractualrequirementsand actualsituation arealways thesame. There isa matchingperformed onthecontractualrequirementsversus actualsituation.

Detective

ITdependent

N/A Futuremarket

Partly The application monitors dealsagainst the contractualrequirements (thresholds) as setin the system. Expectations arealerted to the Mid-Office.

Follow up ofexceptionalerts relatedto legalcontractrequirements.

Within legalrequirements, wenote pricings,quantities, datesandcounterparties.

We note that(most)applications willhave limitedsupport.

As described, specific ITGC’s are not in scope of the thesis

41

5. ConclusionThe main objective of the thesis was to come up with an internal continuous control monitoring framework forelectricity trading. To come to such a framework, we posed the following sub questions:

Which risks can be identified in energy trading? Which controls can be identified to manage the identified risks? Using the controls identified, can a best practice framework for internal control monitoring in energy

trading be defined?

Which risks can be identified in electricity trading?Electricity trading refers to the buying or selling of electricity. Due to among others the perishability ofelectricity, volatility in supply and demand, changing legislation and counterparty dependencies trading ofelectricity is inherently uncertain and does thus poses risks. We have identified the following risk categories inenergy trading:

Risk category Inherent RiskLevel

Description

Credit risk High The risk of financial loss due to counterparties not beingable to pay their obligations.

Market risk High The risk of financial loss due to changing circumstances inthe market.

Liquidity risk High The risk of suffering loss due to costly conversion of illiquidassets into cash.

Operational risk High The risk of financial loss due to ineffective internal controls .

Physical delivery risk High The risk of financial loss due to unbalance prices as a resultof a mismatch between agreed and delivered supply.

Regulatory risk Medium The risk of financial loss due to non-compliance to laws andlegislation.

Legal (contract) riskMedium The risk of financial loss due to non-compliance with

contractual requirements.

For more details on the risks, please refer to the framework or paragraph 2.3.1.

Which controls can be identified to manage the identified risks?In total 42 controls were identified which together can manage the 7 risk categories identified. The controls areincluded in paragraph 4.2.

Using the controls identified, can a best practice framework for internal control monitoring in electricitytrading be defined?We have defined a framework based on continuous control monitoring for electricity trading. The completeframework can be found in paragraph 4.2. However to be able to implement this framework a control orientedapproach and a mature control environment is needed, both with relation to trading and IT General Controls.Limitations in IT environment, resources, risk awareness, industry accepted risk appetites and control-orientedemployees are mentioned as issues in putting the defined continuous monitoring controls into practice. At themoment, it can thus be concluded that the current control environment at electricity traders is not matureenough to leverage on the added value of continuous control monitoring. Referring to IBM’s maturity levels asfor continuous monitoring as discussed in paragraph 1.2, companies must first implement and maintain aAutomated/Corporate wide aggregation maturity level (level 3) before a maturity can be achieved in whichdecisions are driven by business performance matrix (level 4). First, a control based internal control frameworkshould be implemented, before continuous monitoring can be applied effectively. This is further discussed inthe following chapter.

42

6. ConsiderationsWhile performing the analyses and interviewing energy trading experts, trading companies and externalauditors of energy trading companies we noted several current limitations and additional considerations. Wehave grouped these considerations in 3 categories, namely:

Organizational reform and awareness

Cost /benefit analysis per control

Industry control environment

During the case studies we also noted that not all controls were perceived as ‘value adding’. We encouragefurther research on each control to assess the costs and benefit per control.

Technical limitation of the CCM framework should together with a cost / benefit analysis be research in afurther study to gain insight in the added value of the CCM framework.

Organizational reform and awareness

Applying a continuous control monitoring internal control framework requires several prerequisites. For anorganization to leverage the advantages of an continuous control monitoring framework, the organizationsstructure should be embedded in such a way that key control monitoring is utilized throughout theorganization. An effective (but not only) option is the three lines of defense model as discussed earlier.However, in such a model all roles and responsibilities should be clearly defined. During our case study andexpert interviews we noted that not all energy traders have an three lines of defense model embedded in theorganization, or use a key control oriented framework for internal control monitoring. Implementing a differentorganizational structure and developing a key control based framework requires serious investments andmanagement attention.

The primary goal of the continuous control monitoring framework is to prevent risks not in line withmanagement’s risk appetite, or correct them as quickly as possible if occurred. We note that managements riskappetite is key in control monitoring, as the risk tolerance determines what are outliers or not. Earlier wediscussed that preventative application controls were mostly perceived by the business of lesser added valuesince these were perceived as ‘limiting the traders’. Although traders indeed need to have space to trade, havingno limitations they pose a great risk for the continuity of the company. There are numerous examples of tradecompanies in which this risks that were insufficiently managed caused great impact to the company. Refer tolarge energy traders such as Enron, Dynergy and Williams17. A lower management risk appetite and/or amindset change towards a more control oriented internal control environment is required to lowermanagements risk appetite and/or a company’s risk levels. We noted that the electricity trading industry is anindustry in which results dependent on the ability the quickly make trades. This reduces the priority for(preventative) controls in the organization to let traders trade as fast as possible. Based on historic events (e.g.refer to the list of highest trading losses18) however we noted that traders can abuse this freedom. As such,questions can be raised regarding the industries risk appetite as a whole. The question thus remains if thecontrol environment should be changed on individual traders level or at industry level. We encourage furtherresearch on this fundamental question. Upcoming legislations and the usage of regulated clearing houses maylimit parts of the risk, but as long as management’s risk appetite is high, bonus culture encourages risk takingand no additional preventive controls such as four eyes principles, limit structures, etc. are not implemented,future risks may not be effectively managed.

Cost/benefit per control

In this thesis we provide a basic framework for risks affiliated with electricity trading, the control objectives thatshould be performed to manage the risks, and a way to implement the control objective by means of a

17 http://www.economist.com/node/1273641, accessed 26-08-201218 http://en.wikipedia.org/wiki/List_of_trading_losses, accessed 26-08-2012

43

continuous monitoring control. Depending on management risk appetite, portfolio’s, own generation versustrading, volatility of demand and supply, ETRM design, etc. different companies may have different risklandscapes. As such, different controls or implementations may be most effective in managing risk. In addition,some controls may not provide additional comfort, while they demand a considerable effort for implementationor can be effectively monitored by a periodic manual control. We therefore stress the importance of acost/benefit analysis per control before implementation of continuous control monitoring. A detailed riskevaluation should always be the first step in defining the internal control environment.

Current maturity of IT environment

Continuous control monitoring requires a high level of maturity of the IT environment and IT General Controls.As most IT environments are not ready to implement continuous controls, serious management commitmentmay be required to reach a higher internal control maturity level. To facilitate continuous control monitoring inelectricity trading, two requirements must be met:

1. The relevant information must be centrally stored and available

2. The system must be able to facilitate continuous control monitoring

Based on our work performed during the case study and expert interviews we conclude that at current mostenergy trading companies have not developed the required level of maturity to perform all proposed controls.As such, questions can be raised regarding the possibility of continuous control monitoring on therecommended scale. Most controls identified assume a complete and accurate registration of all trades andtheir confirmation, while electronic conformation is not always available. New legislation such as EMIR,requiring central settlement of OTC trades, sets a step towards complete and accurate registration. We furtherurge ETRM suppliers to take note of the framework created and to develop controls with can increase the levelof control of trading companies. However, without demand from the trading companies itself and thusuncertainty regarding payoff of investments, it is less likely that ETRM suppliers themselves initiate furtheractions to move towards continuous control monitoring.

Overall assessment of industry readiness

An important aspect of applying continuous control monitoring is the availability, completeness and accuracy ofthe underlying information. As various sources have acknowledged that to date not all trades are confirmed bycounterparties, completeness of the trades is still an issue. In addition, limitations are noted to the current ITenvironment, the calculation of the VaR may take two hours to finish, a preventative control calculating a VaRbefore a trade would therefore not be realistic without traders being significantly delayed. Additionally anumber of controls are also dependant on data (exchange) from other parties. A white list of traders indicatingthe maximum amount allowed to be traded with counterparties has for example to be implemented at anexchange platform. Currently the exchange platforms only show traders anonymously.

Based on these findings we consider the electricity trade market as a whole is not mature enough to effectivelyapply continuous control monitoring other then the VAR and liquidity at risk calculations throughout theindustry. Limiting to apply other continuous control monitoring controls are first the structure of the market. Alarge number of the controls are dependent for example on the information provided by brokers like the creditlimits per counter party. Secondly, as long as management risk appetite is not changed, proposed controls canbe perceived as limiting the traders. If no environment exists with a focus on risk management in electricitytrading, controls are hard to be implemented, for example refer to the COSO model19 as evidence of effectiverisks management.

19 http://www.coso.org/documents/cosoicifoutreachdeck_05%2018%2012.pdf , accessed latest 26-08-2012

44

7. Implications for EDP AuditIn this thesis we have created a framework for electricity traders and have discussed the perceived added valueof the framework. This thesis is written from an EDP audit perspective (refer to paragraph 1.6), the impact andimplications of the findings of this thesis on the EDP audit domain will be discussed in this paragraph. Inparagraph 1.6 the primary goal of an (IT) audit is explained; the reduction of uncertainties. To perform anaudit, objectives are needed that describe the ‘soll’ position (to be). The main contribution of this thesis is thatthis thesis described the ‘soll’ position by defining objectives to reduce uncertainties.

One implication of this thesis for EDP auditing is that the majority of controls in place can be automated andmade continuous. Based on the three lines of defence model as described in paragraph 2.3 we note that the firstand second line of defence will also need a more in depth knowledge of IT audit and of testing of controls. Forthe third line the changes needed in the first and second line also has implications. As internal control withinorganization increases, the approach of an audit changes. More reliance for example could be placed on the testwork of the first and second line. Initially educating the first and second line to perform and document controlsmay prove to be a challenge.

Although the framework created challenging to implement on short term (refer chapter 6 for theconsiderations), in the future, the framework could be applied at electricity traders. The difficulties ofimplementing the framework on short term could, except for the technical limitations, also be a direct effect ofthe premature current level of control (environment), refer to the considerations in chapter 6. Traders forexample use ‘trading speed to be competitive’ as argument against preventative controls. Based on the casestudies performed we noted that the freedom of traders outweigh the perceived benefits of controls. Asdiscussed in chapter 6, due to the current low availability of preventative controls, potentially every trader canmake trades which are not in line with the company risk appetite. This is also illustrated by historic events asEnron, which could happen again based on the current (lack of) control environment. Please note that thisthesis has been written from a case study perspective. It could be argued that the control environment of thewhole industry should be changed in order for traders to be able to change their control environment. Theimplications for (EDP) auditors in this case in that the auditor should demand a level of control environmentincluding preventative controls. In case (all) auditors demand a certain level of control environment at theelectricity traders, the competitive disadvantage of traders with preventative controls disappear. This couldlead to an industry which is in better control of the trade activities without individual traders having acompetitive advantage over other traders by saving money / time on controls.

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Bellino, C., Hunt, S. (2007). “Global Technology Audit Guide (GTAG) 8: Auditing Application Controls”. TheInstitute of Internal Auditors. ISBN 978-0-89413-613-9

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Appendix I - Scoping

Category ScopingTrade market Energy tradingType of energy ElectricityType of trading Electronic trading (Forward & Spot market)Type of trader Supply chain and Asset Backed tradersEnergy trade type End – BuyerRegulatory environment NetherlandsLines of defence All three linesControl type Continuous Control MonitoringIT general controls Not in scopeGeneral ledger accounting Not in scope