© 2014 Risk Edge Solutions 1

TABLE OF

CONTENTS

3 Blog 1: The Humble PI of Risk Management

9 Blog 2: Commodity Risk Management beyond

VaR

15 Blog 3: Review: Report on Economics of

Commodity Trading Firms

f int © 2014 Risk Edge Solutions 2

BLOG

1The Humble PI of Risk Management

A lot of approaches and frameworks get discussed in companies

that are relatively new to implementing Risk Management

processes – the types of control processes, committee structure,

risk reports, estimation methods, quantification models, systems,

people, etc. In fact, these discussions sometimes go so intense

and deep that it all becomes a big circle that keeps coming back to

itself, and eventually nothing moves. If you’ve ever been a part of

such an implementation, you too would be painfully aware of this

fact.

One of the ways to simplify things while starting a new Risk

Management Initiative in the company, or while revamping an old

one is, to focus at the heart of a Risk Management Function – its

purpose. And at the core of a Risk Management Function is a

powerful, yet Humble PI of Risk Management – the Probability –

Impact (PI) matrix.

INTRODUCTION

Blog 1: The Humble PI of Risk Management

© 2014 Risk Edge Solutions 4f int

For energy and commodity trading companies, each of the risks

can be positioned in a PI Matrix. The risks could be as subjective

as key-person risk (key trader / senior management leaving the

company) and reputation risk, or more quantifiable like market /

price risk, liquidity risk, currency risk, interest rate risk or

somewhere in-between – like credit risk, default risk, etc. What’s

most important for any organization is identifying which risks are

most relevant to them and try to estimate their probability of

occurrence.

This exercise is usually done based on the past experiences &

data, and future plans of business growth. For quantifiable risks,

it’ll be easier to estimate the probabilities of occurrence as there

are several models available to help you do that. But for other

types of risks, companies generally take the qualitative / subjective

estimation approach. Generally, the first few tries will not be ideal,

and it is important to accept that it is a process, which will

eventually evolve into a useful framework if the entire team keeps

at it.

What is the Humble PI Matrix?

Blog 1: The Humble PI of Risk Management

© 2014 Risk Edge Solutions 5f int

Once the probabilities of those risk events have been arrived at,

the next step is to calculate the impact each of those events would

have on the P&L in case they materialize. It is okay to start with a

good approximation here rather than aiming for precision – which

can only be achieved over a period of time. Combining the

probability and impact of each risk event gives their position in the

PI Matrix as given below:

Understanding the PI Matrix

Blog 1: The Humble PI of Risk Management

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The policies, control processes, systems and reports should then

be designed as per the position of the risk event – for example, all

risk events falling under Quadrant 1 should be actively managed,

which means there needs to be a sophisticated system, reporting

process, regular review and control process built around those

events.

On the other hand, those falling in Quadrant 2 may just be actively

monitored, which means they too need to have their limits

monitored on a regular basis, with only periodic hedging / de-

hedging / trading decisions taken to reduce these risks.

Risk elements in Quadrant 3 just need passive monitoring, which

means risk events falling under this quadrant need to be assessed

at periodic monthly / quarterly basis or on an as-and-when basis

since for these, both the probability of occurrence and impact on

P&L are low.

Risk Events in Quadrant 4 need to be managed with limits and

some kind of quantification, but not so actively – since these risks

have relatively lesser impact on the P&L.

Understanding the PI Matrix (Contd.)

Blog 1: The Humble PI of Risk Management

© 2014 Risk Edge Solutions 7f int

The PI Matrix, therefore, is not a solution in itself for Risk

Management, but it certainly gives companies a good starting

point. But far more importantly, what it does is send a message

across the organization to focus on a few risk events rather than

all of them. Unless the organization has a well-established Risk

Function, it is rarely ever possible for them to manage all the risks

equally – those who attempt to do so, realize rather painfully that

they are actually managing none of the risks. The PI matrix helps

you choose your battle, the most important ones first – and that is

what makes it one of the most powerful starting points in Risk

Management.

CONCLUSION

Blog 1: The Humble PI of Risk Management

© 2014 Risk Edge Solutions 8f int

BLOG

2Commodity Risk Management beyond VaR

But it doesn’t tell you what your position / portfolio could lose

beyond that confidence. If you are the one managing risks in your

organization using the VaR framework, chances are you would

already know this. So the real question is, is there and extension

of Commodity Risk Management beyond VaR that solves this

problem? Expected Shortfall (ES) is part of the answer to this

question. Let us take a look at the Issues with using only VaR and

how Expected Shortfall can help us overcome those issues to a

certain extent.

INTRODUCTION

Blog 2: Commodity Risk Management beyond VaR

First, the bad news –

Value at Risk (VaR) of

a position / portfolio

just gives the

maximum loss you

can have, with a

certain confidence.

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Let’s start by putting some numbers around this problem.

Consider a simple, 1 commodity portfolio below:

The portfolio is currently worth $ 250 mn with a M2M P&L of $ 4.1

mn. However, the VaR, which is calculated for 1-day Holding

period and 95 percentile confidence level, is greater than M2M

P&L, and is $ 4.5 mn. This means that on 19 out of 20 days the

position is not expected to have a M2M Loss exceeding $ 4.5 mn

(which is fairly bad by itself, since it can easily wipe out the entire

M2M P&L !). But on 1 out of 20 days (on an average) this M2M

loss will exceed that figure. But, to what extent can this loss be

above $ 4.5 mn? This is a question that VaR does not answer. On

that 1 very bad day, the loss could be anything above $4.5 mn.

Issue with using only VaR

Blog 2: Commodity Risk Management beyond VaR

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Now, while it is comforting (and in compliance with regulations for

a lot of industries) to know what portfolio’s worst loss could be on

19 out of 20 days (on an average), it is severely discomforting to

NOT know how much could the portfolio lose on that 1 very bad

day ! Could it be $ 5mn, or 10, or 50? It makes a huge difference,

right?

Issue with using only VaR (...contd.)

Blog 2: Commodity Risk Management beyond VaR

© 2014 Risk Edge Solutions

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And now for some good news, this is where Expected Shortfall

(ES) comes to our rescue. ES, also sometimes known as

Conditional VaR or Expected Tail loss, tells us how big the number

could be on that 1 very bad day. To represent it mathematically,

where,

X is the random variable of loss and

α is the confidence percentile, 0<α<1, (α=0.05 in our case, since

we are using 95th percentile for VaR)

The above equation is just a complicated way of saying that ES for

a position at any confidence level is an expected to be greater

than or equal to the VaR for that position at that confidence level !

And here is how the ES is calculated:

This equation too, is just a complicated way of saying that ES is

an average of VaRs, between 0 and α confidence levels !

Commodity Risk Management beyond VaR :

Expected Shortfall

Blog 2: Commodity Risk Management beyond VaR

© 2014 Risk Edge Solutions 13f int

With this knowledge, let’s look at the same table again:

Now, with this new knowledge it is easier for any Risk Manager to

commit about the loss on that 1 very bad day, which is $ 7.8 mn. It

might be less or more for the organization, depending upon

several factors including risk appetite, but this knowledge surely

extends our comfort to an area where VaR doesn’t reach.

There are still a lot of questions left unanswered though, like:

• How can I be sure that the worst case loss (under any

circumstance), will not exceed ES?

• How do I determine whether the ES amount is less or more for

my organization?

• How does knowledge of ES impact my Risk Policy?

These and many other such questions will be dealt with in our

future posts.

CONCLUSION

Blog 2: Commodity Risk Management beyond VaR

© 2014 Risk Edge Solutions14f int

BLOG

3Review: Report on Economics of Commodity

Trading Firms

According to Reuters, “The firm approached Craig Pirrong, a well-

known professor of finance and commodity markets commentator

at the University of Houston, last July to commission an

independent review of the commodity trading industry, with the

goal of “demystifying” it. The resulting 63-page report, based on

public filings and interviews with around 10 senior Trafigura

traders and a number of C-level executives last September,

reached a conclusion similar to several previous reports: relative

to Wall Street banks, merchant trading companies’ size, function

and balance sheets make them far less likely to be sources of

systemic risk.” (contd.)

Our Review

Blog 3: Review: Report on Economics of Commodity Trading Firms

The recent release of Report on

Economics of Commodity Trading

Firms is a bold step from a well-

respected Commodity Trading Firm

(CTF) – Trafigura, in giving an

insight and “demystifying” the

secretive commodity trading

industry. And it is indeed a very

impressive read.

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The Report focusses on how Commodity Trading Firms are

exposed to various risks – Price, Basis, Spread, Liquidity, Credit,

etc. and how they manage those risks. Risks are interspersed

across the value chain of any commodity business, and the report

emphasizes the need for commodity trading firms to have Risk

Management practices in place. Notably, following points are very

unique to this report:

• There is very low, at times negative, correlation between

quantities of various commodities (2001 – 2011). Given that

Commodity Trading Firms are exposed to volumes more than

prices, it shows that there are huge benefits from

Diversification that firms can achieve – to reduce the

variability of firm’s risk.

• Integration across the value chain gives firms the ability to

self-hedge and absorb the shocks to a certain extent.

• Trafigura has invested USD 550 mn over the last 3 years in

Risk Management and Measurement Systems. According to

the report “The increasing data and analytical intensity of

trading and risk measurement modeling is tending to

increase the degree of these scale and scope economies.”

(contd.)

© 2014 Risk Edge Solutions

Blog 3: Review: Report on Economics of Commodity Trading Firms

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• Trafigura measures Risk using Monte-Carlo method that

combines 5000 risk factors, at 95% confidence level, for 1

day holding period and its VaR limit is less than 1% of Group

equity.

• Along with VaR, the firm uses Expected Shortfall (Conditional

VaR) along with qualitative measures to broaden the scope of

Risk Management.

These along with several such data points and perspectives make

this a must read for all Commodity Trading Firms. You can read

the original report here:

We have put the original report on our site for your benefit.

Read it here.

© 2014 Risk Edge Solutions

Blog 3: Review: Report on Economics of Commodity Trading Firms

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What is Risk Edge?

A Publication of

Software

Risk Consulting

Define and Implement Risk Policy, with an aim to

lower the Total Cost of Risk (TCoR). Customized Risk

Training programs to align internal people with their

role in risk management better

RiskEdge Software

An Integrated Risk Analytics Platform for Commodity

companies. It is Easy-to-use, highly Configurable, and

really Cost – Effective. It automates Risk Processes

and enables Deeper Business Insights.

Consulting

© 2014 Risk Edge Solutionsf int

A Giant leap in Commodity

Risk Management

Try RiskEdge !

Why RiskEdge?

1. Advanced Risk Platform built specifically for Commodity Companies.

2. It is easy to integrate with existing systems

3. Flexible to configure and easy to use.

4. For most companies, it can be set-up in under 3 weeks !

5. Multi-Dimensional Analysis capabilities

6. RiskEdge Algorithm Library (REAL) - To integrate your own Algorithms

7. Web-based system, built with latest technology

© 2014 Risk Edge Solutionsf int