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  1. 1. 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 timeV a number that the chief executive of any energy trading company shouldknow before leaving the officeeach day. It indicates howmuch money the company firms have begun using what they consider better mea- sures of market risk, all of which are based on VaR. Three of these new measures are Profit at Risk (PaR), Earn- ings at Risk (EaR), and Cash under normal market condi- tions. The final VaR commu- nicated to top management at the end of each day is aggregated from the individ- ual VaRs of the company’s different trading desks andcould 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 describespany can close its position. four sections that follow. the three main techniques forOriginally used in finance, Specifically, VaR measures calculating VaR: analytic VaR,VaR was adapted years ago the worst expected loss (for Monte Carlo, and historicalby energy trading compa- a portfolio) to a specified con- simulation.nies to meet their needs. fidence level (usually 95%) Just how useful is VaR for12 Global Energy Business, May/June 2001
  2. 2. Risk managementenergy companies? In thesecond section, Chal Barn-well points out its weak- Calculating andnesses. He says that if ener-gy companies use VaR the using Value at Riskway that banks use it, theycould grossly underestimate o s t r i s k m e a s u r e m e n t level for the set.their risk exposure. The bet-ter measure of market riskthat his company has comeup with is a variation of VaR M methodologies are based on the analysis of a set of There are three main methodologies for calculating Value at Risk (VaR): vari- scenarios that describe pos- ance-covariance (also known as analytic sible future “states” of the VaR), Monte Carlo simulation, world. Each of the method- B Y D R . C ARLOS and historical simulation. The B LANCOcalled PaR. o l o g i e s m a k e s d i ff e r e n t three are complementary, but assumptions about the possible evolution each offers a different view of portfolio Also recognizing the limi- of markets, but they follow a similar risk. Ideally, a firm would use all threetations 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- gate those results to form a distribu- Variance-covarianceduce another measure of tion and extract the mean and variabilitymarket risk—EaR—that they of the profit, portfolio value, or revenue The most commonly used of the VaRsay is more suitable for phys-ical-asset-intensive energy 1. Monte Carlo simulationcompanies. The articleincludes an example of how Current forward Volatility andmanagers of a hypothetical curves, other prices rrelation informationgas-fired merchant powerplant might use EaR to limit Scenario generation enginetheir company’s market riskexposure. 4 1 2 Finally, in the fourth section,Louis Guth and KristinaSepetys explain how their New prices for scenario 1 for scenario 2 scenario 10,000company’s patent-pendingC-far model can provide an Hypothetical MTM for each price scenarioanswer to a question thatenergy trading firms often ...... –$645,334 –$243,000 $543,000ask: “What is the probabil-ity that this year’s cash flow 160will be inadequate to fund 140 Monte Carlo simulation VaRour strategic investments?” 120 No. of occurrencesThe answer takes the form 100of a risk profile, which the 80model generates by taking 60into account the different 40types of risk to which a com- 20pany is exposed. 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 millionGlobal Energy Business, May/June 2001 13
  3. 3. 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 lossesNo. of occurrences MTM of the portfolio 100 under each scenario of a portfolio based on how it would 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 the P&L distribution that it does not use estimated vari- 40 ances and covariances, and does not 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- s 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 s 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
  4. 4. Risk managementwith the risk of events that are veryunlikely to occur but could lead to 3. Extreme value theory applied to value at risk 1.6catastrophic losses. Yet traditional EV-VaR vs. normal VaRVaR calculation methods tend to ignore 1.4 Historical Cumulative probabilities, %extreme events and focus on risk mea- 1.2 simulationsures that accommodate the entire resultsempirical distribution of returns. This 1.0is a problem, because it is extreme 0.8events—like a large market move—that VaR based on extreme VaR based value distributionproduce the largest losses. 0.6 on normal distribution Using stress tests and scenario analy- 0.4ses to simulate the changes in the valueof a portfolio under hypothetically 0.2extreme market conditions is no solu- 0.0tion, because they cannot explore all –35 –30 –25 –20 –15 –10 –5 0possible scenarios. What’s more, such Loss, %analyses do not indicate how likely it isthat extreme events will occur. companies has been limited to a pas- is more complicated—and interest- The problems resulting from extreme sive role—for reporting rather than ing—than risk management in mostevents are not unique to risk manage- prescriptive purposes. Now, howev- other fields because electricity and gasment; they also arise in disciplines such er, firms in dynamic industries— production and trading are physicalas hydrology and structural engineer- such as energy—are beginning to use, where extreme events can have it for more proactive purposes—for As energy markets become moredevastating consequences. Researchers risk management rather than just risk complex, energy firms are embracingand practitioners in these fields handle measurement. Active VaR can iden- risk management systems and proce-the problem by using extreme value tify business activities that incur too dures, such as Value at Risk, to becometheory (EVT). EVT is a specialist much risk relative to their level of more competitive. sbranch of statistics that derives general return. It can also point out whichproperties of the tail, or extreme end, of assets, business processes, and activ- Dr. Carlos Blanco is manager of globala distribution by making the best possi- ities are increasing or decreasing the support and educational services atble use of a limited set of its realized corporation’s overall risk exposure. Financial Engineering Associates,extreme values. By focusing on the Most risk professionals agree that ( Berkeley, Calif.extreme tail of a distribution, VaR can risk management in the energy industrybe estimated with a confidence ofgreater than 95%. The difference EVT makes to VaRestimates is illustrated in Figure 3,which shows the tail of the West TexasIntermediate (WTI) daily return distrib- Profit at Risk:ution from 1983 to 1999. The dots indi-cate the actual extreme return observa-tions, the continuous line by the right More realistic thanvertical axis represents the tail (assum-ing that logarithmic returns follow anormal distribution), and the other con- Value at Risktinuous line represents the tail of anextreme value distribution fitted to ompanies that generate or Volume riskthese data. The message this figure con-veys is that the confidence level ofEVT-calculated VaRs is much higherthan that of VaRs calculated using tradi-tional methodologies. C deliver electricity, or sell load-following ancillary ser- The financial risks estimated by typ- vices or retail load services, ical VaR calculations are wholly relat- need a more accurate and robust risk-measurement met- B Y C HAL ed to market price movements. However, the real and increas- B ARNWELL ric than the “pure” form of ingly complex energy industryToward risk management Value at Risk (VaR). Profit at Risk engenders additional financial risks (PaRTM) is a more useful measure for a that VaR cannot measure. The mostTraditionally, the use of VaR within variety of reasons. important of these is volume risk,Global Energy Business, May/June 2001 15
  5. 5. Risk managementwhich occurs whenever a power plant 1.Breakpointhas an unplanned outage, customers usemore power than expected, or a trans-mission line fails. 150 Volume risks—and their impact on Relatively 80 Delivery pricevalue and profit—are not caused by stable could vary forward significantlymarket price movements, but rather by prices 60 from thephysical problems, such as those men- forward pricetioned above. If anything, the converse expectationis true: Market price movements may 30result from volume risks. In fact, the Feb 00 May 00 Sep 00 Feb 01 June 01biggest and most shocking market price Break pointmovements in power markets can all betraced back to physical volume risks. change dramatically. After a certain the correlations between risk factors and date, or breakpoint, prices become dri- the volatility of these factors. The rela-Liquidity risk ven by less benign factors such as tionships are derived from econometric weather, the short-term imbalance estimates based on historical data and/orCompanies that use VaR to measure the between supply and demand, plant out- inferred from current market variables—impact of market price movements on ages, and transmission system bottle- options prices, for example.the value of their asset portfolios typ- necks. The environment becomes much The PaR approach considers both theically rely on a “stop loss” strategy. more volatile. For energy trading firms, volatility and shape of the forward priceThey believe that they can quickly VaR remains a valid way to measure curves observed historically during theclose out their position to limit the their risk exposure before the break- delivery period, as well as projecteddamage from negative market price point date. However, for many other price curves and volatility for that timemovements. However, the time required energy companies—including genera- frame. The PaR metric, then, reflectsto close a position depends on market tors, retail distributors, and entities the entire exposed profit of a portfolioliquidity—but liquidity changes over involved in complex marketing transac- position, not just the change expected intime and fluctuates in response to sud- tions with load following optionality— it for a brief period of time.den shifts in market prices. In imma- PaR offers a way to more accuratelyture power markets, price spikes can measure their risk. PaR in practicebe extreme and cause severe market liq-uidity problems. PaR in theory Short of selling a portion of a gener- In practice, therefore, closing a posi- ating unit, a company has no way totion is neither as easy nor straightfor- The biggest difference between PaR effectively close out the position rep-ward as in theory. Because many real- and VaR is that the former assumes resented by a plant without assumingworld contracts do not comply with mar- that positions will be taken through to potentially more risk than is hedged.ket standards, they are not easy to sell or delivery rather than closed out prior The plant will be subject to forced out-trade. The alternatives—selling off gen- to the breakpoint. Because this mea- ages, transmission constraints, gen-eration assets or portfolios of retail cus- sure takes into account the full finan- eration derates, and many other volu-tomers—are equally unrealistic. cial risk of highly volatile spot prices, metric variables that are uncontrollable. it is a far more realistic approach. In many cases for strategically locat-Physical delivery and The foundation of PaR is simulation- ed facilities, forced outages can drivebreakpoints based modeling, which basically tests spot prices to abnormally high points the entire range of risks affecting spot at exactly the wrong time for theWhat makes assessing risk in power prices at the time of delivery. The model hedger—when the facility is down. Inmarkets complex is that market prices follows a pseudo-Monte Carlo approach these circumstances, a VaR measurechange drastically as the date of phys- that re-bases spot price simulations to based on a short holding period of twoical delivery approaches. Prior to sev- match forward curve prices and expect- or three days would give the impres-eral months before that date, price ed spot volatility. The Monte Carlo sim- sion that a plant’s risk is minor whenbehavior is driven by relatively benign ulation method calculates the change in it is actually quite substantial.factors—such as economics, forward the value of the portfolio using a sample For example, calculating the VaR of afuel prices, and the market’s anticipated of randomly generated price scenarios Cinergy 500-MW gas-fired plant ongeneration level. This is considered the that are assumed to be equally probable. May 30, 2000, would produce a figureholding period. The approach requires making assump- of $5.2 million for the June through However, as the date of delivery tions about market structures, the sto- September period. But using the PaRapproaches, the drivers of market prices chastic processes the prices follow, and metric, the calculation of total profit16 Global Energy Business, May/June 2001
  6. 6. Risk managementexposure for this same unit (based upon situation was the volumetric uncertainty derivative contracts, and to that of a com-a 95% confidence level) would produce of load, which effectively prevented bined portfolio of derivative contractsa very different figure—$69 million. utilities from closing out their retail and the merchant plant.What this means is that leaving the plant positions. And to make matters even For the price simulation, assume thatcompletely unhedged for the premium worse, regulatory rules made it illegal derivative prices follow geometricsummer months and selling its output on for them to hedge their positions sub- Brownian motion with a monthly terma daily basis would have resulted in an stantially. structure of volatility and drift, and spoterosion of mark-to-market (MTM) prof- The PaR metric would not have prices follow a Markov regime-switch-itability of $69 million by the end of helped California’s utilities out of their ing model with each regime character-August. regulatory dilemma. But it might have ized as having geometric Brownian The shortcomings of VaR—in particu- flagged the potential for eroding profits motion with mean reversion. Thelar its assumption that past events are in a simulated environment and provid- regime-switching model for spot pricesreliable indicators of the future—are ed an accurate measure of their risk allows for the large discontinuousperhaps most glaring if one considers exposure. s jumps observed in electricity prices.the California crisis. No amount of his- Figure 1 depicts a single simulationtorical data on wholesale prices could of forward and spot prices for powerhave predicted what happened in the Chal Barnwell is vice president of the during a typical summer in North Houston operations of KWI (,spring of 2000—events that haven’t America. Spot prices follow the highly London.recurred since. Further complicating the volatile purple line at the front of the graph. Forward contracts are lessEarnings at Risk: 1. Sample simulation of power pricesBetter for asset owners ccounting rules prohibit ener- the profit function. The distribution ofA gy companies with portfo- profit captures the upside potential—as lios of physical assets B Y D R . G ARY well as the downside risk—of from marking these D ORRIS AND variability of market prices, asassets to market. Nor can these A NDY D UNN well as the operational charac-companies liquidate or acquire teristics and reliability of physi- Table 1. Generation dispatch inputs andtheir assets as quickly as companies cal assets. A well-constructed EaR outputswith purely financial portfolios. For model assesses the impact of alternative Operational inputs Totalthese two reasons, energy companies derivative trading strategies on both Load. . . . . . . . . . . . . . . . . . . . . . . . . 130are embracing Earnings at Risk (EaR) tails of the profit distribution curve. Heat rate . . . . . . . . . . . . . . . . . . . 13,653as a more appropriate measure of mar- Minimum uptime, hours . . . . . . . . . . . . . 2 Minimum downtime, hours . . . . . . . . . . —ket risk than Value at Risk. EaR in action Startup costs, $/MW of capacity . . . . . . 1 Startup time, hours . . . . . . . . . . . . . . . . 1 Shutdown cost, $/MW of capacity . . . 229EaR defined How might EaR be used in the ener- Shutdown time, hours . . . . . . . . . . . . . . 1 gy industry? Consider the hypotheti- Ramp-up rate, MW/hr . . . . . . . . . . . . . . 3Calculations of EaR follow calcula- cal case of E-Corp., a U.S. firm that Rampdown rate, MW/hr . . . . . . . . . . . 15tions of profit, using the following owns a 130-MW gas-fired merchant Maximum starts per month . . . . . . . . . 40 Maximum continuous run time/equation: plant and has a marketing/trading affil- month, hours . . . . . . . . . . . . . . . . 744 Profit = Spot price revenues generat- iate that does both spot and forward Expected forced outage rate . . . . . . . 0.10 ed by the organization’s assets transactions in electricity and natural Expected outage duration . . . . . . . . . . . 2 Variance in outage duration . . . . . . . . . . 1 – cost of production, gas. E-Corp.’s objective is to manage Outage minimum, days . . . . . . . . . . . . . 1 + hedge and other instrument its exposure of earnings to market risk Outage maximum, days . . . . . . . . . . . 120 payoffs prior to delivery, over a 12-month period. First, simu- Summary output Total + hedge and other instrument late prices, and then estimate the EaR Generation, MWh . . . . . . . . . . . . 773,500 Gross revenue, $1,000 . . . . . . . . 120,157 payoffs during delivery. of E-Corp.’s unhedged portfolio. Next, Net revenue, $1,000 . . . . . . . . . . . 54,043 A Monte Carlo simulation can pro- compare the EaR of this unhedged Net revenue, $/kW . . . . . . . . . . . . . . 415vide an estimate of the distribution of portfolio both to that of a portfolio of Capacity factor, % . . . . . . . . . . . . . . . . 68Global Energy Business, May/June 2001 17
  7. 7. Risk managementvolatile and turn flat during delivery. Of Statistics: 2. Distribution of net revenuescourse, this is only a single simulation EaR (C.1. 95%) 11,109,200 100of prices; a fair assessment of risk Mean 54,034,300 Maximum 74,851,500 90requires several thousand simulations. Minimum 32,599,900 Probability of occurrence 80 Given a series of price simulations Std. dev. 7,497,690and the operating attributes for the 70 Bin data:plant, the dispatch of electricity as well 60 Level Net revenueas the corresponding revenue informa- 0 32,599,900 50tion can be simulated for the year by 1 37,721,800 40mapping each hour of each day’s spot 5 42,925,100price for power and natural gas to the 10 44,797,200 30 20 47,002,200potential generation of electricity. Table 20 30 49,445,1001 tabulates the operational attributes of 40 51,543,600 10this plant, its expected generation, 50 53,826,900 0capacity factor, and expected revenues. 60 56,053,800 3.26E7 3.68E7 4.11E7 4.95E7 5.8E7 6.43E7 6.85E7 7.27E7 However, Table 1 provides only a 70 57,773,800 6.01E7 Net revenues, $portion of the information needed foreffective risk management and budget- of owned assets, commercial contracts, set the volatility of the earnings of theing. The rest of the data needed must and/or hedge positions. Suppose that in power plants. When these portfolios arecome from an examination of the distri- addition to the merchant plant, E-Corp. aggregated into one exposure, overallbution of net revenues. Figure 2 depicts has a strip of natural gas forward pur- risk is reduced to $3.5 million, based onthe probability density function (PDF) chases (1 million mmBtu) and electricity the offsetting profits of the plant and theand other key statistics for the sales (130 MW). A typical risk manage- derivative contracts.unhedged power plant. The statistical ment activity would be to measure the The example clearly shows the hazardssummary on the left indicates that the VaR or EaR of this portfolio alone with- of not fully considering all assets that areplant’s expected net revenues are esti- out considering the generation asset. subject to market risk in risk analysis. Ifmated at $54 million, and that its However, the generation plant has the E-Corp. had measured the portfolio ofEarnings at Risk figure is $11.1 million. potential to offset some of the risk of this forward contracts in isolation, it wouldIn other words, you would expect that portfolio of forward purchases and sales. have inaccurately estimated market risk,the plant will generate $54 million in Table 2 illustrates that both the gener- and that could have led management tonet revenues, and it is 95% certain it ation plant in isolation and the portfolio pursue an erroneous hedge strategy. Thewill generate at least $42.9 million. of forward contracts carry significant example underscores the need for energy Once the numbers have been deter- levels of EaR ($11.1 million and $13.9 companies to apply sophisticated model-mined, you can measure the market risk million, respectively). In the profit equa- ing techniques to all of its market-price-with respect to the earnings of a portfolio tion for EaR, the derivative contracts off- sensitive assets using a method that esti- mates risk to earnings. Table 2. Risk-reducing effects of aggregating From VaR to EaR generation with forwards Generation plant alone Forward contracts alone Combined portfolio Since they came out of the world of EaR (C.I. 95%) . . 11,109,200 EaR (C.I. 95%) . . 13,853,430 EaR (C.I. 95%). . . 3,518,400 finance, methods of measuring market Mean . . . . . . . 54,034,300 Mean . . . . . . . . 2,091,030 Mean . . . . . . . 56,209,200 risk have evolved in sophistication Maximum . . . . 74,851,500 Maximum . . . . 24,442,800 Maximum . . . . 63,966,700 and robustness, from basic VaR to Minimum . . . . . 32,599,900 Minimum . . . . (18,228,000) Minimum . . . . . 50,635,900 measures, such as EaR, that integrate Std. dev. . . . . . . 7,497,690 Std. dev. . . . . . . 7,861,020 Std. dev. . . . . . . 2,351,140 the risks of financial instruments and Bin data Bin data Bin data those of physical assets. The benefits Level . . . . . . Net revenue Level . . . . . . Net revenue Level . . . . . . Net revenue of using such advanced methodolo- 0 . . . . . . . . . 32,599,900 0 . . . . . . . . (18,228,000) 0 . . . . . . . . . 50,635,900 gies extend from improving a compa- 1 . . . . . . . . . 37,721,800 1 . . . . . . . . (16,786,800) 1 . . . . . . . . . 51,047,100 ny’s leverage capacity to stabilizing 5 . . . . . . . . . 42,925,100 5 . . . . . . . . (11,762,400) 5 . . . . . . . . . 52,690,800 10 . . . . . . . . 44,797,200 10 . . . . . . . . (7,955,730) volatility in its earnings to enhancing 10 . . . . . . . . 53,384,000 20 . . . . . . . . 47,002,200 20 . . . . . . . . (4,674,190) 20 . . . . . . . . 54,248,900 its stock-price-to-earnings ratio. s 30 . . . . . . . . 49,445,100 30 . . . . . . . . 49,445,100 30 . . . . . . . . 54,954,900 40 . . . . . . . . 51,543,600 40. . . . . . . . . . . 193,180 40 . . . . . . . . 55,537,500 Dr. Gary Dorris is CEO, and Andy Dunn is 50 . . . . . . . . 53,826,900 50 . . . . . . . . . 1,577,460 50 . . . . . . . . 56,050,700 vice president of risk management of 60 . . . . . . . . 56,053,800 60 . . . . . . . . . 4,574,250 60 . . . . . . . . 56,561,400 e-Acumen (, San 70 . . . . . . . . 57,773,800 70 . . . . . . . . . 6,001,630 70 . . . . . . . . 57,106,300 Francisco.18 Global Energy Business, May/June 2001
  8. 8. Risk management lish whether a company has adequateCash Flow at Risk cash reserves to service its debt in the event of an earnings jolt. What’s more, because it can inform decision-makingfor non-financial about purchasing insurance and other hedging strategies, C-far could become a benchmark for corporate risk man-companies agement. Tailored for the industry ash Flow at Risk (C-far) fy reported risk in securities liti- For power companies, the C-far pack-C By Louis Guth is a patent-pending diag- and Kristina gation and disclosure docu- age comprises the model and a data nostic developed by Sepetys ments. Investment bankers and base of 10 years’ worth of quarterly National Economic venture capitalists will find it a cash flow and other financial dataResearch Associates (NERA) econo- useful method for analyzing investment for approximately 100 electric utili-mists. This analytic model is designed opportunities. ties. The model includes a statisticalto forecast earnings volatility one methodology for transforming thesequarter to one year ahead. It may be C-far vs. VaR data into peer-benchmarked risk mea-used to enhance planning and capital surements. It can construct tailored,investment, as well as insurance and Banks, insurers, investment firms, and forward-looking cash flow distribu-hedging strategies. It derives from a other financial companies have long tions for a utility’s quarter-ahead andbroad statistical analysis of earnings used Value at Risk, or VaR, to manage year-ahead horizons based upon largeresults and other factors for the entire portfolio risk. VaR measures how much samples of cash flow shocks experi-universe of publicly held non-finan- the value of financial assets—foreign enced by a set of peer-group firms.cial companies. C-far starts with the currency, equities, commodities, These samples are large enough tological premise that risk reveals itself bonds—will drop in a day or a week allow making relatively precise state-in non-financial companies as devia-tions from cash flow. C-far promises to enhance financialWhat and who it’s for strategy and long-term investment planningThe introduction of the C-far model hascoincided with what has become a if they are affected by a market rever- ments about the probability of onevery big season for unanticipated earn- sal. The C-far model can be thought in 10-year or one in 20-year bad out-ings shortfalls at some of the country’s of as a form of VaR for measuring comes. The distributions also allowlargest companies, including many aggregate risk against a company’s quantification of the probability ofutilities. The model can provide answers cash flow. Whereas the VaR method specified adverse cash flow shocks—to two questions that should be fore- takes a bottom-up approach to quan- for example, “What is the likelihoodmost in the mind of any CFO or high- tify the risks caused by individual that cash flow falls by 50% in thelevel manager concerned with accu- financial assets, C-far looks directly next year?”rately assessing value: at cash flow under the assumption that Such information is of great interest s “What is the probability that this all risks to a corporation are manifest to investors, who are naturally con-year’s cash flow will be inadequate to in shocks to expected earnings. cerned about volatility in reported quar-fund our strategic investments?” Because the C-far model creates for- terly earnings. By disclosing the results s “What is the worst thing that can ward-looking probability distributions of a C-far analysis to investors or secu-happen to the company financially in for a company’s cash flow, it provides rity analysts ahead of time, a companythe next quarter or year?” a measure of the likelihood of rare neg- can help put earnings shocks into a The C-far model considers every con- ative events that could produce a sig- credible, objective, peer-benchmarkedceivable type of risk to which a compa- nificant drop in earnings. As a tool for perspective. sny can be exposed and produces a risk corporate treasurers and CFOs, C-farprofile that can help answer these criti- promises to enhance financial strategy Louis Guth is senior vice president, andcal questions. Its applicability goes and long-term investment planning. Kristina Sepetys is senior consultant (Sanbeyond volatile industries—such as But the model can also be used to help Francisco office) at National Economicenergy, manufacturing, and high-tech companies assess their capital structure Research Associates (, White Plains, Lawyers can use it to clari- and credit-worthiness. C-far can estab-Global Energy Business, May/June 2001 19
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