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Managing Physical and Financial

Uncertainty

By Bjornar Eide

Director of Risk Management Sempra Energy Utilities

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Presentation / Discussion Overview

Framing the issue

Definition of uncertainty

Understanding impact of uncertainty

Structural methods available to quantify impact of uncertainty

Mitigation techniques available to address uncertainty

Strategic approach to “value of hedging” or/and mitigation of uncertainty

Formulation of hedging objective and financial/risk parameters

The importance of the “Cash Flow Base”

Timing and execution/trigger techniques

Hedging philosophy, organizational capability (programmatic, semi-dynamic, dynamic)

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FRAMING THE ISSUE

• Learning how to live with and embrace uncertainty has become an integral part of

strategic thinking in the US energy sector over the past 5 years due to the following

drivers;

• Divergence of business models

• State of deregulation / competitive environment

• Volatile fuel and regulated markets/environment

• Pressure on decreasing foreign energy dependence (physically and financially)

• Renewable energy focus

• Building business models with sustainable growth potential

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DEFINITION OF UNCERTAINTY

• “The expected and un-expected variability caused by internal and external factors

that drive earning and/or rates”

– Expected variability refers to normal market events (i.e. change in market prices)

quantifiable with standard risk measures at statistical confidence levels (VaR, EaR,

CFAR) to portray the probable impact.

– Un-expected variability refers to market events (i.e. event of default, price spikes, etc)

quantifiable with extreme value theory, stress tests, scenario analysis to describe the

potential impact.

– Internal factors refers to what can be reasonably controlled through internal governance

and pro-activeness in positioning, contractual formulations, etc.

– External factors refers to sudden changes in regulatory direction/focus, tax credits,

market dynamics, volatility, etc.

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UNDERSTANDING IMPACT OF UNCERTAINTY

• Important to understand the financial impact of expected and un-expected uncertainty on the following;

• Business Models / Strategic direction

• Earnings

• Rates

• Major projects

• Main Questions

– Does our organization / regulated environment have the financial strength to withstand expected uncertainty (probable ) and un-expected uncertainty (potential) ?

– How does the risk appetite and financial situation help describe the desired risk philosophy and governance structure of your organization ?

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Impact of Uncertainty (2)

• Potential for adverse outcomes drives how the Risk Philosophy of the organization addresses

how to deal with expected and unexpected uncertainty.

• Risk adversity/appetite not a goal in itself, but rather a consequence of quantifiable studies,

capital allocation and insurance against bad outcome.

• Capital markets expectancy for company identity and return generation can help formulate

positioning for expected and unexpected uncertainty.

• Uncertainty can also lead to higher value if identified and utilized to flexibly respond to

unfolding events – real options

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Real options View

Ownership and

control of Managerial

Options Increase

ValueVal

ue

Uncertainty

Traditional view

___________________________________________

“real options” by Martha Amram and Nalin Kulatilaka

Cone of Uncertainty

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Structural methods available to quantify

impact of uncertainty

• Business Models / Strategic Direction

– Constant Financial Benchmarking and scorecards to evaluate strategic flexibility and direction

– Identify top strategic dependencies to enhance internal focus

– Sustainability testing of Earnings Growth projections

• Earnings

– Develop robust quantification/standardization of earnings component base through cash flow mapping

techniques

– Mark to market accounting capability or/and data-ware house capability.

– VaR, EaR, CFAR on an incremental basis for each component and across earnings base.

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Structural Methods (Continued)

• Rates

– Simulation of rate behavior utilizing Monte Carlo Techniques or/and descriptive inputs(rate matrix)

– Identify tolerance band to be managed structurally with the help of standard Risk Measure (VaR, EaR,

CFAR)

– Monitor or create programs to manage rates

• Major projects

– Identify project variables

– Calculate an actuarial project VaR for top identified variables

– Translate project risk measures impact to financial metrics of project

– Monitor project VaR

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Mitigation techniques available

to address uncertainty

• When choosing potential mitigation techniques for uncertainty it becomes essential to

differentiate the approach between expected uncertainty and un-expected uncertainty.

• Mitigation techniques for expected uncertainty (probable) can be considered economic hedging

and are typically liquidity intensive and measured on the basis of immediate economic

impact/effect on the hedged items (delta, hedge ratio, dollar offset).

• Mitigation techniques for unexpected uncertainty (potential) can be considered insurance and

hedging are typically liquidity constrained and measured on the basis of insurance coverage /

guarantees associated with adverse events and therefore will typically not meet the same

stringent GAAP hedge effectiveness rules (Fair Value, Cash-Flow Hedge).

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Strategic approach to “value of hedging” or/and

mitigation of uncertainty

• Measurement of success an important issue up front in communication with process stakeholders.

• Important aspects to consider are as follows;

– Is the hedge in place to secure earnings/rates/asset value / project financials where expected risk could results in adverse outcome ? (to protect fair value)

– Is the hedge in place to secure earnings/rates/asset value/ project financials where un-expected risk could result in adverse outcome ? (to protect what if scenarios)

– Is it expected over time that hedging/timing decisions should be value added/accretive on a stand alone basis ?

• Comparison against alternative portfolio benchmarks

• What are the expectations for return on capital allocation / liquidity allocation

– Does the portfolio contain type of positions (fair value) that if not actively managed will diminish with time or/and can not be realized in the cash market (theta)

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The hedging objective should identify the following elements:

1) What are the financial goals of the program (insurance, economic hedge) ?

2) What level of insurance / economic hedge will be provided (minimum, maximum, average guarantees) ?

3) How will compliance with insurance / economic hedge goals be measured (delta, capped, other) ?

4) Will the program cover all types of basis risk (location differences, cash flow expiry etc) ?

5) What are the funding requirements of the program (Max Liquidity, Credit VaR, Budgets etc)

6) What type of program will be put in place (programmatic, semi-dynamic, dynamic) ?

7) What are the triggers for the program (market, weighted cost of rates etc) ?

8) What are the organization execution guarantees put in place to support type of program ?

Formulation of a recommended approach should also include a review of the following:

Earnings/rate aspiration in combination with stakeholder expectations (Markets)

Expected Peer Competitiveness

Liquidity Utilization (Max)

Total Potential Cost/Impact of Program

Formulation of hedging objective and

financial/risk parameters

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Hedging Alternative Quantification

Alternative #1 Alternative #2 Alternative #3

Liquidity

(Max in Millions)

$ 10.00 $ 15.00 $ 12.00

Delta

(Initial in Bcf)

12.00 11.00 13.00

Delta

(Max in Bcf)

20.00 20.00 20.00

Hedge Program

(Cost in Millions)

12.00 12.00 - 15.00 11.00 - 25.00

Rate Level

(Stress + 30%)$ 11.45 $ 12.45 $ 10.85

Rate Level

(Stress – 30%)$ 7.45 $ 8.45 $ 7.85

All numbers in the table have been randomly generated

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The importance of the “Cash Flow Base”

• Cash flow mapping of linear and non-linear exposure vital for incremental understanding of

potential impact of hedge programs to cash flow base.

• Cash flow base capability should enable the organization to break down risk measures into the

following risk views;

A. Periodic components (buckets of time periods)

B. Location break down

C. Position type break down (linear/non-linear)

• Incremental hedges can be assessed by total portfolio impact, impact on option sensitivities

and liquidity (fund usage) to understand impact of proposed hedge programs/mitigation

techniques.

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Timing and execution/trigger techniques

Triggers of hedging programs or incremental hedge tranches can be broken down into the

following categories;

a) Market Price Triggers

i. Price Level

ii. Price level + Risk Factors (95% confidence, 2 standard deviations etc)

b) Fundamental Triggers

i. Days of carry (natural gas inventories), by season

ii. Number of accumulated heating degrees

c) Cost/Revenue Triggers

i. Portfolio VaR triggers (10day, TeVaR)

ii. Weighted cost of rates / IRR, NPV, RAPM

iii. % increase in weighted cost or rates / IRR

iv. Weighted cost of rate / IRR, NPV, RAPM plus risk factors

d) Technical Triggers

i. Momentum indicators (200 Day Moving Average, RSI, Stochastic etc)

ii. Price support/resistance triggers

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Hedging Methods

(programmatic, semi-dynamic, dynamic)

• Hedging methods can be broken into many categories, but what distinguishes the methods is

how they are constructed and the formulation of how dynamic the execution and maintenance

of the programs are. The following categories of hedge methods have been identified for this

presentation;

• Programmatic: The design of a transaction structure consisting of physical/financial

transactions which once executed pose no surprises and are in accordance with the hedging

objective where a predetermined outcome and use of funds has been identified through

simulation / risk slides.

• Semi-Dynamic: The design of programs where the base of the program is static (constant

set of transactions) while incremental parts of the program are determined dynamically by

market or other types of triggers (VaR, Weighted Cost of rates etc)

• Dynamic: The design of programs where the entire program is dynamic and at the discretion

of portfolio manager (s) within certain financial constrains

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Schematic Overview

of Hedging Methods

Programmatic:

Characterized by a

mechanical, predetermined

hedge plan execution based

on expected effectiveness;

no ongoing maintenance;

either predictable use of

funds/liquidity or predictable

total cost to customer (i.e.

hedge-and-hold).

Dynamic:

Leaves the timing and

composition of hedges at the

discretion of the decision

maker; focuses on after-the-

fact success criteria such as

hedge ratio, dollar offset and

RAPM measures; manages

use of funds with budget,

liquidity and/or VaR limit.

Semi dynamic:

Places an initial hedge

tranche with a progressive

ramp up of the hedge volume

dependent on market signals,

probability of price level

and/or expected weighted

costs; some focus on cost

effectiveness and/or use of

funds.

Passive Semi Active Active

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Risk Profile(Programmatic)

SUMMARY: Buy calls to cap 55% of volume

at a predetermined level ($2.50 out of the

money) portrayed by the risk diagram to the

left.

PROS: Execution at initiation with no

maintenance required. Capped liquidity

demand. Very easy to explain – does not

depart from extreme event “insurance”

concept.

CONS: Low delta – possible for customers

to see material cost increases with no benefit

from hedges. Total max rate not guaranteed.

No ability to react to changing conditions.Market Prices

Cu

sto

me

r R

ate

s

Storage Only

Storage + Calls Only

No Hedging

(today)

This is a programmatic example that provides insurance only. Protection primarily

or unexpected uncertainty .

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Risk Profile(Programmatic with financing to cap 55% )

SUMMARY: The program consists of buying

calls of hedge volume at a predetermined level

($2.50 out of the money) financed by ($1.50

out of the money puts) and covered downside

of (-$2.25) portrayed by the risk diagram to the

left.

Similar to Calls only, but:

PRO: Significantly lower premium outlay. In

turn, call strikes can be reduced and premiums

raised, resulting in higher delta (bill protection

at lower price moves). Good approach to

compare with peers at most market points

except the most suppressed prices.

CON: Need to monitor liquidity risk during

price declines. Total settlement value of

derivatives unknown.Market Prices

Cu

sto

me

r R

ate

s

Storage Only

Storage + Calls

+ Put financing

No Hedging

(today)

This is a programmatic example of a hedge that provides both economic and insurance protection

for both expected and unexpected uncertainty.

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Concept:

• Hedging as an integral part of the business for adding value and/or reducing risk

• Detailed simulation of the entire market assuming competition on variable cost

• Simulation of electricity generation, and regional energy flows in the power market in order to

satisfy total demand at minimum costs

Model requirements:

• Detailed description of electricity system

• Updated information on all reservoir levels

• Marginal costs for all power stations

• Detailed and representative inflow statistics

• Detailed information on transmission system, regional load characteristics and variations in

demand over time

Risk Profile – Semi Dynamic(Simulating the Market)

Build internal capability or procure external analysis

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$/Mwh

Time

Today’s physical equilibrium

of natural gas prices

(Mmbtu Value)Reflect the range

of possible future

Price paths and spot prices

in the market

Forecasting fundamental value of

Natural Gas prices (principle)

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Fundamental market analyses require extensive back-testing (example) Production

TWh

52

54

56

58

60

62

64

66

68

70

72

74

100 000

120 000

140 000

160 000

180 000

200 000

220 000

Average spot

price observed

in the market

Actual production

for entire power system

Range of possible

inflow alternatives

Predicted range of

spot prices depending

on production alternatives

$/Mwh

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Summary of Fundamental Approach

• Fundamental view of over-value or under-value against the current market (forward curve)

through quantitative forecasting methods forms the basis for hedging decisions and physical

optimization.

• The approach requires rigid structure and data ware-house capability to support calculation

methodology (regression, stochastic) and forecasting equations.

• Data series needs to be supplied to modeling phase in a very structured fashion.

• The approach requires extensive back testing capability of forecasted data against actual

results.

Semi-Dynamic

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Risk Profile(Semi Dynamic)

SUMMARY: The program consists of buying

OTM calls to bring portfolio capped rate to

55%. Premium cost remains below budget.

Calls are converted in 2 stages to FPIs when

prices exceed preset market price

thresholds, and are re-converted to calls

when market prices fall below the same

levels.

PRO: Up front coverage with catastrophe

insurance at low cost. Deltas increase with

rising markets, yet potential settlement

losses on FPIs expected to be less than

traditional hedge-and-hold strategy.

Converted/re-converted call options

expected to result in reduced premium costs.

Added FPIs can be designed to exceed 55%.

CON: Expected net loss on hedge

instruments. Potential for Call-FPI to skip

widely across the designated threshold

levels or otherwise to cycle back-and-forth

across the level, increasing settlement

losses.

Market Prices

Cu

sto

me

r R

ate

s

Storage Only

Strategy (Expected)

Strategy (Backstop)No Hedging

(today)

Protect against both expected and unexpected uncertainty.

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Risk Profile(Dynamic)

Prices

The program consists of allocating a

progressive VaR (10$ million) to hedging

activity whereby the hedging activity can only

lead to a positive delta impact of portfolio,

but need to guarantee a linear cap at certain

level Unrealized gains of executed hedges a

additive to allocated VaR (25%) while

unrealized losses reduces allowable VaR by

75% stopping out ineffective use of funds

and capital for hedging purposes.

PRO: Market driven and highly visibility

strategy to value of timing of hedges with the

ability to ramp up program driven by timing

success.

CON: Need to monitor VaR and Liquidity

parameters within allocated capital.

$12.50

$6.50

$5.75

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Designing Hedge Programs

Step 1 Frame the Hedging Objective

Step 2 Research possible alternatives

Step 3 Quantify impact of alternatives

Step 4 Recommend best fit alternative

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Hedging Output

(Risk Matrix)

Delta : 120,000 Mwh

Gamma : 30,000 Mwh

Theta :: 250,000 USD

VaR : 2.7 Mill

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Hedging Output(Risk Slide)

Proprietary position example

(Combination of all strategies winter strip)

Data exhibited above for illustrative purposes

The overview can be constructed per strategy

or as a combined strategy of all books

P&L movement of portfolio with each

incremental move of prices

The left hand column shows the financial parameters

of the portfolio while any point within the matrix will

provide information about the value for each financial

parameter given the price level

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BIBLIOGRAPHY

• Bjornar Eide has been a Director of Risk Management for Sempra Energy Utilities since September 2005. Bjornar oversees the risk governance structure for San Diego Gas & Electric and Southern California Gas Company. He is a member of the Risk Management Committee for each of the utilities, which is responsible for managing each of the utility’s exposure to market, credit, liquidity and operational risk. Bjornar has over 13 years of experience from energy markets, serving in a variety of capacities in an international environment. Prior to joining Sempra Energy Utilities, he worked as an independent strategic risk consultant for a variety of clients in Europe and the US focusing on strategic risk management related issues and the design of risk assessment capability. As a Director of Risk Management for NRG (from 2000 – 2002) he built up the risk management department and during his four year tenure with Statoil A/S as a portfolio manager, he actively managed positions that involved petroleum products, crude, natural gas & electricity including the build-up of the power marketing department. Eide holds an MBA in Finance from San Francisco State University and a BA in Business Administration from California Lutheran University.