Laurie Brooks VP Risk Management and Chief Risk Officer Public Service Enterprise Group

1. 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 UNIVERSITYofHOUSTON Global 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

5. Market Risk – Trading vs. Non-Trading Activities Non-Trading Trading • Positions generated by asset/customer business • Strategic “buy and hold” hedges • Positions to facilitate marketing • Proprietary trading positions Purpose • Illiquid or “buy and hold” positions • Holding period measured in months/years • Liquid, actively funded positions across many markets • Holding period measured in days/weeks Liquidity • Asset/customer-driven embedded options • Long holding period makes non-linearity material • Price-driven exchange traded or OTC options • Short holding period allows linear approximations Optionality • Long-term volatilities and correlation • Mean reversion, seasonality simulation, Earnings at Risk • Short-term volatilities and correlation • Jump diffusion, intra-day VaR – analytical, simulation Valuation Risk Management/ Intervention • Structured solutions, contract renegotiations, asset sales and purchases • Management of regulatory process • VaR limit reduction, stop loss limits, hedging with traded instruments

6. Key Concepts of Capital Adequacy: Three Risk Types The framework for determining capital adequacy for economic value requires an estimation of economic capital and thus quantifying the following significant risks: • 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

7. Key Concepts of Economic Capital Adequacy: Market Risk Modeling Approaches Price Behavior Process Market Exposures Pros/Cons Comments Analytical Closed-form approach for modeling price movements Works well for linear type exposures • Pros: • Simple and fast • Easy to change as assumptions change • Cons: • Does not capture optionality well • Minimal ability to model complexities over a longer period of time • 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 outcomes • Cons: • 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. Portfolio Expected Loss (Mean) Probability Credit Economic Capital (Unexpected Loss) Expected Loss (Loss Provisions) Distribution of Portfolio Credit Losses Over a One-Year Time Horizon Confidence Level Key Concepts of Economic Capital Adequacy: Credit Risk 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

9. CA Framework – Key Concepts Key Concepts of Economic Capital Adequacy: Operative Risk – Scorecard 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.

10. CA Framework – Key Concepts Key Concepts of Economic Capital Adequacy:Operative Risk – Risk Taxonomy • 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.

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. CA Framework – Key Concepts Key Concepts – Combined Capital Methodology Description Advantages Disadvantages Assumption Simple Sum Derive economic capital for credit, market, and operative risk, then sum them • Easy to implement • Most conservative view of risk • Overestimates risk • Results in the lowest level of capital adequacy Correlation assumed to be perfect among risk components Modern Portfolio Theory From historical data, determine an explicit correlation among credit, market, and operative risk economic capital Attempts to represent the actual correlation among risks, rather than a conservative assumption Requires a time series of credit, market, and operative risk economic capital that is reasonably robust Assumes that some risks are uncorrelated, allowing for lower risk and improved capital adequacy Monte Carlo Simulation Using consistent parameters, simulate risk factors to produce a joint distribution of outcomes The most robust perspective of risks and their interaction if modeled correctly • Requires a large amount of research, analytical, and technical resources • Ensuring assumptions are correct is critical Material risk inputs can be parameterized accurately

13. CA Framework – Key Concepts 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

14. Example 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 Please refer to pages 61-67 of the white paper for a full description of the example.

15. Example – Setup We chose to model the asset-level impacts over a year of different risks on a company over time. • 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 Generating Plant Power Pool Capacity VOM Heat Rate Fuel Type Book Value Gas-fired combined cycle ECAR 850 3.98 7.25 Natural Gas $510,448,931 Coal-fired, base load NEPool 375 2.51 10.3 Coal $49,720,351 Jet kero-fired peaking PJM 500 34.48 15.7 Jet Kero $11,094,684

16. Example – Setup 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)

17. Example – Setup Credit Risk Calculations Counterparty Rating 1-Year Probability of Default Commodity Counterparty A CCC 27.87% Fuel – coal, natural gas, jet kero Counterparty B BBB 0.34% Power – NEPool, PJM, Cinergy • 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) Counterparty C BB 1.16% Fuel and power

18. Example – Setup 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.

19. Example – Setup Liquidity calculations Liquidity risk is defined as the minimum cash flow point in a simulation. • 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 Monthly cash flow

20. Example – Setup Hedging affects liquidity in offsetting ways. • 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.

21. Example – Methodology Three key methodology choices drive our model Method Pros Cons Joint simulation of credit, market, and operative risks (versus assumed correlations) • Consistency • More data available to check micro relationships rather than portfolio relationship • Can change micro assumption and rerun • Are not assuming answer • Increases memory need and computer time • Necessitates more simplifying assumptions, leading to less accurate estimates of component risks Risk modeling 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 Energy forward prices 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 Daily power prices

22. Example – Pre-Simulation Pre-Simulation: prior to running our simulations, we calculated a number of initial values. Pre-Simulation Calculations • 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

23. Example – Simulation Simulation: we generated the inputs to credit and operational performance. Correlated Market risk simulation* Generation model Market risk forward prices - power Marginal cost of fuel (VOM Correlated & heat rate) forward prices - fuel MTM - A/R - Credit excess/loss A/P on trading contracts Credit risk simulation** Probability of default Operational profit/loss Probability of outage Operative risk simulation** Probability of trader misconduct * 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

24. Example – Results Unhedged Hedged Results – Unhedged vs. Hedged Assets By hedging assets, market risk is reduced by less than the additional economic capital required for credit risk, increasing economic capital adequacy. 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.

25. Example – Results Results – Portfolio Effect Illustration of the mathematical fact:EC = 0 (square root sum of squares) < EC <  < 1 (Monte Carlo simulation) < EC=1 (simple sum) Available vs. Required Capital Sq. Root Monte Carlo ($ millions) Sum of Squares Simulation Simple Sum 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. 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 Economic Capital Adequacy 253.3 251.7 239.9 Diversified Available vs. Required Capital Combined- Total Individual Component ($ millions) 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. Coal Cycle Peaking Assets Total Portfolio 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 Economic Capital Adequacy 6.6 227.1 1.4 235.2 251.7

26. Example – Results 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