Challenges in Capital Adequacy UH-GEMI 3 rd Annual Energy Trading & Marketing Conference: Rebuilding...

Challenges in Capital AdequacyUH-GEMI 3rd Annual Energy Trading & Marketing

Conference: Rebuilding the BusinessHouston, TexasJanuary 20, 2005

Laurie Brooks

VP Risk Management and Chief Risk Officer

Public Service Enterprise Group

UNIVERSITY of HOUSTONGlobal Energy Management Institute

2

Capital Adequacy and Capital AllocationConnected?

• Capital Adequacy – How much capital is required to achieve

the company’s stated goals and objectives?

• Capital Allocation– How should corporations allocate capital

between competing demands?

3

Capital Adequacy for Energy Transactors 1. Capital for what? Business models: regulated utilities, merchant generators, marketing and trading entities Economic capital vs liquidity adequacy Banking models S&P liquidity survey

Measures - EaR vs CFaR, role of stress testing, market risk vs credit risk trade-offs, role of ECE and PFE

2. Why energy is different - impact of following on margin/cash requirements: volatilities sector ratings storability regulatory intervention age and depth of markets contract terms risk mgt tool availability 3. Capital how? Access to capital markets Diversification of cash flows Credit mitigations role of netting and clearing stair stepped margining agts.

4

Capital Use by Activity

Utility Merchant

Generator

Marketer/

Trader

Assets Pipes & Wires, Customers

Generating Facilities

People, IT

Protection Insurance Insurance Insurance, VaR

Maintenance Plant, customer satisfaction

Plant Cash collateral

Growth Acquisition of service territories

New facilities New products, services, markets

Multiple Venture capital Venture capital Venture capital

5

Market Risk – Trading vs. Non-Trading Activities

PurposePurpose

Trading Non-Trading

• Positions to facilitate marketing• Proprietary trading positions

• Positions generated by asset/customer business

• Strategic “buy and hold” hedges

LiquidityLiquidity• Liquid, actively funded positions across

many markets• Holding period measured in days/weeks

• Illiquid or “buy and hold” positions• Holding period measured in months/years

OptionalityOptionality

• Price-driven exchange traded or OTC options

• Short holding period allows linear approximations

• Asset/customer-driven embedded options• Long holding period makes non-linearity

material

ValuationValuation

Risk Management/Intervention

Risk Management/Intervention

• Short-term volatilities and correlation• Jump diffusion, intra-day VaR –

analytical, simulation

• VaR limit reduction, stop loss limits, hedging with traded instruments

• Long-term volatilities and correlation• Mean reversion, seasonality simulation,

Earnings at Risk

• Structured solutions, contract renegotiations, asset sales and purchases

• Management of regulatory process

6

Key Concepts of Capital Adequacy: Three Risk Types

• Market Risk - Variation of portfolio market value due to a change in a market price or rate, as well as a change in energy demand

• Credit Risk - Variation of portfolio market value due to default or a credit downgrade of an issuer or counterparty

• Operative Risk (term to address Operations and Operational risk collectively)– Operations - The risk associated with delivering or producing physical

energy– Operational - The risk of direct or indirect loss resulting from inadequate or

failed internal processes, people, and systems or from external events

The framework for determining capital adequacy for economic value requires an estimation of economic capital and thus quantifying the following significant risks:

7

Key Concepts of Economic Capital Adequacy: Market Risk

Modeling Approaches

Analytical

Price Behavior Process

Closed-form approach for modeling price movements

Market Exposures

Works well for linear type exposures

Pros/Cons

Pros:• Simple and fast• Easy to change as assumptions

changeCons:• Does not capture optionality well• Minimal ability to model complexities

over a longer period of time

Comments

• Works well for determining shorter-term price moves for a trading portfolio

• Can be used as a quick metric to help manage portfolio positions

Simulation Robust methodology for mean reversion, jumps, linking, spot, and forward prices

Full revaluation at each price iteration better approximates nonlinearity of asset/option positions

Pros:• Robust• Captures optionality• Provides a full distribution of

outcomesCons:• Complex to construct the simulation

model• Only as good as model input

parameters• For historical simulation, values are

constrained to conform to history which may be irrelevant due to market, economic, or regulatory changes

• As the time horizon is extended and the need to model certain energy price characteristics increases, simulation becomes a more suitable solution. Meanwhile, the technical difficulties increase and the model needs to be modified to fit the long-term simulation purpose.

8

Pro

bab

ility

Portfolio Expected Loss (Mean)

Distribution of Portfolio Credit Losses Over a One-Year Time Horizon

Credit Economic Capital (Unexpected Loss)

Confidence Level

Expected Loss (Loss Provisions)

Expected Loss– Represents the average loss that a company could expect to incur over a given horizon

Unexpected Loss– Measures the uncertainty of losses around the expected loss

Key Concepts of Economic Capital Adequacy: Credit Risk

9

Scorecard Approach

• Can be used for operations and operational risk to identify risks, determine

frequency and range of costs, and assesses the effectiveness of controls and

mitigation techniques in place. It is subjective, but now that the SEC has

mandated the COSO framework for Sarbanes Oxley 404 compliance, standards

will be set. In particular, the Capability Maturity Model can be adapted to set

standards for a scorecard approach and is already used by many audit firms.

Additionally, a company may want to use CCRO Best Practices from earlier white

papers as a qualitative assessment of where companies stand with regard to

CCRO recommendations.

• Regardless of the scorecard criteria used, a scorecard approach can form the

basis for continuous improvement processes for internal controls to mitigate

operative risk. It can also reflect improvement in the risk-control environment in

reducing the severity and frequency of future losses.

Key Concepts of Economic Capital Adequacy: Operative Risk – Scorecard

CA Framework – Key Concepts

10

• The risk taxonomy is a system for organizing types of operative risks

by serving as a family tree, aggregating risks by various characteristics.

The level of aggregation at which each characteristic presents itself

may be determined individually.

• There is no standardized risk taxonomy, but certain characteristics

should be used to create the groupings:

– Risk classes (people, processes, systems, asset damages) – the broadest

classes of risks

– Subcategories – could include whether the risk is internal or external, a

type of fraud, or a natural disaster

– Risk activity examples – specific activities or events that could cause a

loss, such as rogue trading, hurricane, model risk, or pipeline rupture.

Key Concepts of Economic Capital Adequacy:

Operative Risk – Risk Taxonomy


11

Key Concepts of Liquidity Adequacy• Fixed Payments - This would include, but is not limited to; fixed charges such

as debt service, dividends, debt/equity retirement and current portion of committed, maintenance and non-discretionary capital expenditures.

• Contingent Liquidity – Contingent liquidity is synonymous with unexpected change or variation in liquidity. While economic capital protects against losses in the company’s economic value, contingent liquidity is held to support the risk of unexpected reduction in cash. Includes:

– Cash Flow at Risk– Trigger events:

• Downgrade event– Loss of threshold– Adequate assurance

• Debt/equity trigger– Contingency events:

• Operational/Operations Risk• Credit/counterparty termination default

12

Key Concepts – Combined Capital


Methodology

Simple Sum

Modern Portfolio Theory

Monte Carlo Simulation

Description

Derive economic capital for credit, market, and operative risk, then sum them

From historical data, determine an explicit correlation among credit, market, and operative risk economic capital

Using consistent parameters, simulate risk factors to produce a joint distribution of outcomes

Advantages

• Easy to implement• Most conservative

view of risk

Attempts to represent the actual correlation among risks, rather than a conservative assumption

The most robust perspective of risks and their interaction if modeled correctly

Disadvantages

• Overestimates risk• Results in the lowest

level of capital adequacy

Requires a time series of credit, market, and operative risk economic capital that is reasonably robust

• Requires a large amount of research, analytical, and technical resources

• Ensuring assumptions are correct is critical

Assumption

Correlation assumed to be perfect among risk components

Assumes that some risks are uncorrelated, allowing for lower risk and improved capital adequacy

Material risk inputs can be parameterized accurately

13

Key Concepts – Correlation Math Refresher In a two asset portfolio with equal investment in assets A and B, the VaR of the portfolio (at 95% confidence) VaRA+B = 1.65 * AB where AB is the standard deviation of returns of the

portfolio:

where AB is the correlation between A&B (do the returns move together?)

Remember (a+b)2 =a2+2ab+b2 and

Then if AB =1

So Portfolio VaR = VaRA + VaRB!

If AB=0, (Square root sum of squares)

The truth 0 < AB < 1 lies somewhere in between and:

< AB < A+B

Square root sum of squares Simple Sum

baba 2)(

22 2 BBAABAAB

BABAAB 2)(

22BAAB

22BA


14

The Risk Management team at PSEG demonstrated the

CCRO’s framework using a sample asset portfolio.

• This example illustrates how the CCRO framework can be used in practice

• We will walk you through the following implementation steps:

– Portfolio setup

– Methodology

– Pre-simulation

– Simulation

– Results

• We will also discuss some of the firm and systems resources required

Example

Please refer to pages 61-67 of the white paper for afull description of the example.

Please refer to pages 61-67 of the white paper for afull description of the example.

15

• We modeled market, credit and operative risks jointly in one simulation versus separately– Felt there was better intuition and that we could better justify a choice of the

assumptions – Calculation process seemed clear based on this approach– Used a 1-year holding period and ran 5,000 trials with a 95% CI

• We modeled a five-year time horizon, with price changes modeled as follows:– Year 1: spot– Year 2-5: forward prices

• We chose a variety of assets and parameters.– Three different generating assets and fuel types– Assets are in three different pools

We chose to model the asset-level impacts over a year

of different risks on a company over time.

Example – Setup

Generating Plant

Gas-fired combined cycleCoal-fired, base loadJet kero-fired peaking

Power Pool

ECAR

NEPool

PJM

Capacity

850

375

500

VOM

3.98

2.51

34.48

Heat Rate

7.25

10.3

15.7

Fuel Type

Natural Gas

Coal

Jet Kero

Book Value

$510,448,931

$49,720,351

$11,094,684

16

Market Risk Calculations

• Unhedged market risk

– Minimum [(realized generation over 12 months) + (Expected generation value of the remaining term)] – (Initial expected value of the generation)

• Hedged market risk

– (Unhedged market risk) + (Realized and unrealized trading profit or loss)

Example – Setup

17

Credit Risk Calculations

• Calculated as the sum of credit loss across the twelve months of simulations, as a function of counterparty risk and power pool risk

• The company has three counterparties– Counterparty A is used for fuel procurement– Counterparty B is used for power sales– Counterparty C is used for speculative trading.– The recovery rate is assumed to be 10%.

• Each power pool has collateral requirements that are a function of the company’s credit rating, tangible net worth and activity in the pool– Value is calculated under two potential ratings, BBB (credit limit $80,000,000) and BB

(credit limit $4,000,000)

Example – Setup

Counterparty Rating1-Year Probability

of DefaultCommodity

Counterparty A CCC 27.87% Fuel – coal, natural gas, jet kero

Counterparty B BBB 0.34% Power – NEPool, PJM, Cinergy

Counterparty C BB 1.16% Fuel and power

18

Operative Risk Calculations

• Operations loss

– Sum of lost profit from plants not running at full capacity

• Operational loss (if applicable)

– Hidden trade on the books whose value is set to the largest negative value of all the trading positions on the book.

Example – Setup

19

Liquidity calculations

Prior month realized P/L (retained earnings) Current month generation P/L Collateral posted Accounts receivable Accounts payable Full margin on mark-to-market Credit loss Operations loss Operational loss

Monthlycash flow

Monthlycash flow

Liquidity risk is defined as the minimum cash flow point in a simulation. Liquidity risk is defined as the minimum cash flow point in a simulation.

Example – Setup

20

Hedging affects liquidity in offsetting ways.

Example – Setup

• Liquidity risk is increased by hedging in the following ways

– Creates cash outflows due to full margining on mark-to-market

– Creates the possibility of credit loss

• Liquidity risk is decreased by hedging in the following ways

– Decreases the amount of cash needed to be posted to power pools since that is determined by net activity.

– Decreases the distribution of realized P/L from generation

The net effect of hedging was a decrease in the liquidity risk.The net effect of hedging was a decrease in the liquidity risk.

21

Three key methodology choices drive our model

Risk modelingRisk modeling

Energy forward prices

Energy forward prices

Daily power pricesDaily power prices

Example – Methodology

Method

Joint simulation of credit, market, and operative risks (versus assumed correlations)

Pros

• Consistency• More data available to check

micro relationships rather than portfolio relationship

• Can change micro assumption and rerun

• Are not assuming answer

Cons

• Increases memory need and computer time

• Necessitates more simplifying assumptions, leading to less accurate estimates of component risks

Correlated Brownian Motion for Energy Forward Prices

• Most practical method with 3 power pools and 3 types of fuel for 5 years

• Would be difficult to jointly calibrate more complex model for diversity and tenure of portfolio

• Easier to believe for forward prices rather than spot prices still oversimplifies reality

• Probably overstates volatility for longer-dated contracts

Daily power prices are normally distributed with mean equal to forward price and standard deviation equal to historical daily spot standard deviation

• Allows for analytical determination of MWs of generation and generation value

• No need to do daily simulation

• Ignores operating constraints on plants

• Splitting monthly prices into two normal distributions (normal and extreme days) captures peaking value more accurately

• Does not allow for fuels to vary by day

22

Pre-Simulation: prior to running our simulations, we calculated a number of initial values.

• Initial expected value of the assets

– Calculated based on the current forward prices for fuels and power

• Expected fuel purchases and expected output to be sold to counterparties

– Calculated based on current forward prices

• Randomly-generated positions in power and fuels

– Constrained to be a quarter of the size of outright positions

– Used to simulate a speculative trading operation

Example – Pre-Simulation

Pre-Simulation Calculations

23

Simulation: we generated the inputs to credit and operational performance.

Market risk simulation*

Market risk simulation*

Operative risk simulation**

Operative risk simulation**

* 60 product months x 6 products x 12 monthly steps of random standard normal pulls** 7 risks x 12 monthly steps of uniform random variables pulled

Credit risk simulation**

Credit risk simulation**

Correlatedforward prices

- power

Generationmodel

Marginal costof fuel (VOM& heat rate)

MTM - A/R -A/P on trading

contracts

Probabilityof outage

Probabilityof default

Probabilityof trader

misconduct

Correlatedforward

prices - fuel

Operational profit/loss

Credit excess/loss

Example – Simulation

Marketrisk

24

Available vs. Required Capital ($ millions) BBB Rated BB Rated

Available Capital 571 571 Debt 286 286

Required Economical Capital Market Risk 23 23 Credit Risk 0 0 Operative Risk 22 22

Diversification Effect - Across Risks -11 -11

Total Required Economic Capital 35 35

Economic Capital Adequacy

251 251

Sources of Liquidity 600 400

Fixed Payments 200 200

Contingent Liquidity 27 27

Liquidity Capital Adequacy 373 173

Available vs. Required Capital ($ millions) BBB Rated BB Rated

Available Capital 571 571 Debt 286 286

Required Economical Capital Market Risk 6 6 Credit Risk 16 16 Operative Risk 22 22

Diversification Effect - Across Risks -13 -13

Total Required Economic Capital 30 30


255 255

Sources of Liquidity 600 400

Fixed Payments 200 200

Contingent Liquidity 0 7

Liquidity Capital Adequacy 400 193

Results – Unhedged vs. Hedged Assets

Example – Results

Unhedged

Hedged

Note: the simulation was also run with all counterparties set at BBB to reflect the average rating of many portfolios. The credit risk remained at zero with a 95% confidence level, while market risk was reduced from $23 million to $6 million.

By hedging assets, market risk is reduced by less than the

additional economic capital required for credit risk, increasing

economic capital adequacy.

By hedging assets, market risk is reduced by less than the

additional economic capital required for credit risk, increasing

economic capital adequacy.

25

Results – Portfolio Effect

Example – Results

By analyzing capital requirements for unhedged assets as part of a portfolio vs. individually, the example illustrates how diversification reduces the economic capital required for market and operative risks.

Sq. Root Sum of Squares

Monte Carlo Simulation Simple Sum

Net Assets - Debt 285.6 285.6 285.6

Required Economical Capital

Market Risk 22.5 22.5 22.5

Credit Risk 0.0 0.0 0.0

Operative Risk 23.2 23.2 23.2

Diversification Effect - Across Risks -13.4 -11.8 0.0

Total Required Economic Capital 32.3 33.9 45.7

253.3 251.7 239.9

Available vs. Required Capital ($ millions)


CoalCombined-

Cycle PeakingTotal Individual

Assets Total Portfolio

Diversified Component

Risk

Net Assets 49.7 510.4 11.1 571.3 571.3

Debt 24.9 255.2 5.5 285.6 285.6

Required Economical Capital

Market Risk 7.0 27.6 3.5 38.1 22.5 -15.7

Credit Risk 0.0 0.0 0.0 0.0 0.0 0.0

Operative Risk 22.3 3.4 2.3 27.9 23.2 -4.7

Diversification Effect - Across Risks -11.1 -2.9 -1.6 -15.6 -11.8 3.8

Total Required Economic Capital 18.2 28.1 4.1 50.5 33.9 -16.5

6.6 227.1 1.4 235.2 251.7

Available vs. Required Capital ($ millions)


Illustration of the mathematical fact:EC = 0 (square root sum of squares) < EC < < 1 (Monte Carlo simulation) < EC=1 (simple

sum)

Disclaimer: the closeness of the Monte Carlo (MC) and Square Root Sum of Squares is not representative. In general, one shouldn’t assume that SRSS is a good proxy for MC.

26

Why Emerging Practices?• These are recommendations for internal use and experimentation for companies to better

understand and quantify the capital and cash requirements of the merchant energy business; these are not recommendations for external communication or new disclosure.

• No one is going to implement all of these recommendations over night.• Most of us have some capability to begin looking at the components of Capital Adequacy

and liquidity requirements through the use of tools that we already have in place but which require extension and modification to achieve the more sophisticated views that result from the white paper recommendations. This should be a controlled evolutionary process - in most cases, the less sophisticated tools that we already have in place generate more conservative answers than the sophisticated approaches do.

Why we will implement these ideas over time:• Better than what we have now• Emphasize need to look both long term and short and to look at cash flow as well as

earnings and value• Ideas and methodologies useful in decision making

Example – Results

Challenges in Capital Adequacy UH-GEMI 3 rd Annual Energy Trading & Marketing Conference: Rebuilding...

Documents

Transcript of Challenges in Capital Adequacy UH-GEMI 3 rd Annual Energy Trading & Marketing Conference: Rebuilding...