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Risk Management Risk management Deregulation and competition increase the volatility of energy prices. The more volatile an energy market is, the riskier it is for firms doing business in that market. Energy traders call this risk market risk, and some now quantify it using measures other than, but based on, the original one—Value at Risk Value at Risk: Variations on a theme alue at Risk (VaR) is Since then, some trading during a given period of time a number that the firms have begun using what under normal market condi- chief executive of they consider better mea- tions. The final VaR commu- any energy trading sures of market risk, all of nicated to top management V company should which are based on VaR. at the end of each day is know before leaving the office Three of these new measures aggregated from the individ- each day. It indicates how are Profit at Risk (PaR), Earn- ual VaRs of the company’s much money the company ings at Risk (EaR), and Cash different trading desks and could lose if market prices Flow at Risk (C-far). The mea- portfolios. In the first section, move wildly before the com- sures are described in the Dr. Carlos Blanco describes pany can close its position. four sections that follow. the three main techniques for Originally used in finance, Specifically, VaR measures calculating VaR: analytic VaR, VaR was adapted years ago the worst expected loss (for Monte Carlo, and historical by energy trading compa- a portfolio) to a specified con- simulation. nies to meet their needs. fidence level (usually 95%) Just how useful is VaR for 12 Global Energy Business, May/June 2001 Risk management energy companies? In the second section, Chal Barn- Calculating and well points out its weak- nesses. He says that if ener- gy companies use VaR the using Value at Risk way that banks use it, they could grossly underestimate ost risk measurement level for the set. their risk exposure. The bet- methodologies are based There are three main methodologies ter measure of market risk on the analysis of a set of for calculating Value at Risk (VaR): vari- scenarios that describe pos- ance-covariance (also known as analytic that his company has come M sible future “states” of the VaR), Monte Carlo simulation, up with is a variation of VaR world. Each of the method- BY DR. CARLOS and historical simulation. The BLANCO called PaR. ologies makes different three are complementary, but Also recognizing the limi- assumptions about the possible evolution each offers a different view of portfolio of markets, but they follow a similar risk. Ideally, a firm would use all three tations of VaR are Dr. Gary approach towards measuring market methods to obtain the most accurate pic- Dorris and Andy Dunn. In risk: First calculate a profit or rev- ture of the market risk it faces. the third section, they intro- enue for each scenario, and then aggre- duce another measure of gate those results to form a distribu- Variance-covariance tion and extract the mean and variability market risk—EaR—that they of the profit, portfolio value, or revenue The most commonly used of the VaR say is more suitable for phys- ical-asset-intensive energy 1. Monte Carlo simulation companies. The article includes an example of how Current forward Volatility and managers of a hypothetical curves, other prices rrelation information gas-fired merchant power plant might use EaR to limit their company’s market risk Scenario generation engine 4 exposure. 1 2 Finally, in the fourth section, Louis Guth and Kristina Sepetys explain how their New prices for scenario 1 for scenario 2 scenario 10,000 company’s patent-pending C-far model can provide an Hypothetical MTM for each price scenario answer to a question that energy trading firms often ...... ask: “What is the probabil- –$645,334 –$243,000 $543,000 ity that this year’s cash flow 160 will be inadequate to fund 140 Monte Carlo simulation VaR our strategic investments?” 120 The answer takes the form 100 of a risk profile, which the 80 model generates by taking 60 into account the different types of risk to which a com- occurrences of No. 40 pany is exposed. 20 0 -1.258 -1.146 -1.034 -0.922 -0.810 -0.698 -0.586 -0.473 -0.381 -0.249 -0.137 -0.025 0.087 0.199 0.311 0.424 0.536 0.648 0.760 0.827 0.984 —Anne Ku Portfolio profit or loss, $1 million Global Energy Business, May/June 2001 13 Risk management 2. Historical simulation ical profits and losses, or P&L, of the firm’s portfolio under each scenario are converted into a histogram of Current Data base with historical forward expected profits and losses, from which market prices curves, exchange rates, interest rates VaR can be calculated (Figure 1). One advantage of Monte Carlo simu- lation is that it does not assume that 2 portfolio returns are distributed normal- ly. Another is that it is a forward-look- New set of prices New set of prices New set of prices for scenario 1 for scenario 2 ...... for scenario 100 ing assessment of risk that takes into 1 account options and non-linear posi- 3 tions. However, the methodology requires the use of a correlation and ...... volatility matrix to generate the random scenarios, and that makes it computa- tionally intensive. It also requires the company to have pricing models for all the instruments in its portfolio. Estimated P&L Estimated P&L Estimated P&L 4 –$1,215,334 –$443,000 $643,000 Historical simulation 160 Historical simulation refers to the 140 Historical simulation VaR The estimated P&L for each scenario is the process of calculating the hypotheti- 120 difference between the cal distribution of profit and losses MTM of the portfolio of a portfolio based on how it would 100 under each scenario and the current MTM. If have behaved under several hundred 80 you group all possible scenarios in the past. Its advantages are 60 P&Ls, you can get that it does not use estimated vari- the P&L distribution ances and covariances, and does not No. of occurrences of No. 40 assume anything about the distribution 20 of portfolio returns. However, the big 0 -1.258 -1.146 -1.034 -0.922 -0.810 -0.698 -0.586 -0.473 -0.381 -0.249 -0.137 -0.025 0.087 0.199 0.311 0.424 0.536 0.648 0.760 0.827 0.984 disadvantage of historical simulation Portfolio profit or loss, $1 million is its assumption that future risks are much like past risks, and that is less calculation methodologies is based to derive a “synthetic” portfolio of assets frequently the case in today’s fast- on an analysis of the volatilities of, and held. The synthetic portfolio is made up changing energy environment. correlation among, the different risk of (cash flow) positions in the risk fac- To calculate VaR through historical exposures of the firm’s portfolio. tors, or “vertices,” whose volatilities and simulation, one needs two things: a data The calculation of analytic VaR is a correlations are known. base with historical prices for all the two-step process: The purpose of cash flow mapping is risk factors to be included in the simu- I Select a set of market risk factors to find the “best” replication of a finan- lation, and pricing models to re-evalu- and systematically measure actual price cial exposure for the purpose of measur- ate the portfolio for each price scenario levels, volatilities, and correlations. ing its risk in conjunction with the firm’s (Figure 2). Historical simulation could I Put the firm’s exposures into a other exposures. The most difficult part be considered a special case of Monte form that can be analyzed using risk of this step is defining a set of risk fac- Carlo simulation in which all scenarios factor information. This is called cash tors that is small enough to be manage- are defined before the event according flow mapping. able, but comprehensive enough to cap- to the past behavior of market prices. In Market risk factors refer to anything ture all the firm’s risk exposures. Once the case of electricity and over-the- that affects the value of the portfolio. the cash flow map has been created, one counter energy markets, it is quite diffi- Prices are the most common market fac- need only perform basic matrix manipu- cult to calculate VaR using historical tors, but you could also include in the lation to calculate the VaR of a portfolio. simulation because price histories are analysis non-market-related risks—such hard to come by. as volume and weather. To be compati- Monte Carlo simulation ble with the available risk factor data, A new way to calculate VaR every instrument in a portfolio needs to Monte Carlo simulation generates ran- be reduced to a collection of cash flows dom pricing scenarios. The hypothet- Risk managers are primarily concerned 14 Global Energy Business, May/June 2001 Risk management with the risk of events that are very 3. Extreme value theory applied to value at risk unlikely to occur but could lead to 1.6 catastrophic losses. Yet traditional EV-VaR vs. normal VaR 1.4 VaR calculation methods tend to ignore Historical extreme events and focus on risk mea- 1.2 simulation sures that accommodate the entire results empirical distribution of returns. This 1.0 is a problem, because it is extreme 0.8 events—like a large market move—that VaR based on extreme value distribution VaR based produce the largest losses.
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