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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



V         a number that the
          chief executive of
          any energy trading
          company should
know before leaving the office
each day. It indicates how
much 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 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-
well points out its weak-
                                         Calculating and
nesses. He says that if ener-
gy companies use VaR the                 using Value at Risk
way that banks use it, they
could 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 risk
that his company has come
up 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 LANCO
called 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 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-
                                         gate those results to form a distribu- Variance-covariance
duce another measure of                  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
                                                                                                                      Scenario generation engine
their company’s market risk
exposure.                                                                                                                                                                                        4
                                                   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                                                                                                                         ......
                                                                    –$645,334                             –$243,000                                                                       $543,000
ask: “What is the probabil-
ity that this year’s cash flow                                160
will be inadequate to fund                                    140      Monte Carlo simulation VaR
our strategic investments?”                                   120
                                         No. of occurrences




The answer takes the form                                     100
of a risk profile, which the                                  80
model generates by taking
                                                              60
into account the different
                                                              40
types of risk to which a com-
                                                              20
pany 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 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
No. 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
Risk management
with the risk of events that are very
unlikely to occur but could lead to
                                                               3. Extreme value theory applied to value at risk
                                                                             1.6
catastrophic losses. Yet traditional                                                  EV-VaR vs. normal VaR
VaR calculation methods tend to ignore                                       1.4
                                                                                                                            Historical




                                               Cumulative probabilities, %
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
                                                                                                                                                    VaR based
                                                                                                        value distribution
produce the largest losses.                                                  0.6                                                                    on normal
                                                                                                                                                    distribution
   Using stress tests and scenario analy-
                                                                             0.4
ses to simulate the changes in the value
of a portfolio under hypothetically                                          0.2
extreme market conditions is no solu-
                                                                             0.0
tion, because they cannot explore all                                           –35      –30      –25        –20          –15         –10          –5              0
possible scenarios. What’s more, such                                                                              Loss, %
analyses do not indicate how likely it is
that 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 most
events are not unique to risk manage-        prescriptive purposes. Now, howev-                                      other fields because electricity and gas
ment; they also arise in disciplines such    er, firms in dynamic industries—                                        production and trading are physical
as hydrology and structural engineer-        such as energy—are beginning to use                                     processes.
ing, where extreme events can have           it for more proactive purposes—for                                         As energy markets become more
devastating consequences. Researchers        risk management rather than just risk                                   complex, energy firms are embracing
and 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 become
theory (EVT). EVT is a specialist            much risk relative to their level of                                    more competitive. s
branch of statistics that derives general    return. It can also point out which
properties of the tail, or extreme end, of   assets, business processes, and activ-
                                                                                                                      Dr. Carlos Blanco is manager of global
a distribution by making the best possi-     ities are increasing or decreasing the                                   support and educational services at
ble 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                                    (www.fea.com) Berkeley, Calif.
extreme tail of a distribution, VaR can      risk management in the energy industry
be estimated with a confidence of
greater than 95%.
   The difference EVT makes to VaR
estimates is illustrated in Figure 3,
which shows the tail of the West Texas
Intermediate (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 than
vertical axis represents the tail (assum-
ing that logarithmic returns follow a
normal distribution), and the other con-
                                             Value at Risk
tinuous line represents the tail of an
extreme value distribution fitted to                   ompanies that generate or Volume risk
these data. The message this figure con-
veys is that the confidence level of
EVT-calculated VaRs is much higher
than 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 industry
Toward 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 most
Traditionally, the use of VaR within         variety of reasons.                        important of these is volume risk,
Global Energy Business, May/June 2001                                                                                                                              15
Risk management
which occurs whenever a power plant                        1.Breakpoint
has an unplanned outage, customers use
more power than expected, or a trans-
mission line fails.                                                                                                  150
   Volume risks—and their impact on            Relatively
                                                          80                                                               Delivery price
value and profit—are not caused by             stable                                                                      could vary
                                               forward                                                                     significantly
market price movements, but rather by          prices     60
                                                                                                                           from the
physical problems, such as those men-                                                                                      forward price
tioned above. If anything, the converse                                                                                    expectation
is true: Market price movements may                                                                                   30
result from volume risks. In fact, the                         Feb 00   May 00     Sep 00      Feb 01    June 01
biggest and most shocking market price                                                             Break point
movements in power markets can all be
traced 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/or
Companies 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 the
ically rely on a “stop loss” strategy.        more volatile. For energy trading firms,      volatility and shape of the forward price
They believe that they can quickly            VaR remains a valid way to measure            curves observed historically during the
close out their position to limit the         their risk exposure before the break-         delivery period, as well as projected
damage from negative market price             point date. However, for many other           price curves and volatility for that time
movements. However, the time required         energy companies—including genera-            frame. The PaR metric, then, reflects
to close a position depends on market         tors, retail distributors, and entities       the entire exposed profit of a portfolio
liquidity—but liquidity changes over          involved in complex marketing transac-        position, not just the change expected in
time 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 accurately
ture power markets, price spikes can          measure their risk.                           PaR in practice
be 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 to
tion 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 assuming
world 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 drive
breakpoints                                   based modeling, which basically tests         spot prices to abnormally high points
                                              the entire range of risks affecting spot      at exactly the wrong time for the
What makes assessing risk in power            prices at the time of delivery. The model     hedger—when the facility is down. In
markets complex is that market prices         follows a pseudo-Monte Carlo approach         these circumstances, a VaR measure
change drastically as the date of phys-       that re-bases spot price simulations to       based on a short holding period of two
ical 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 when
behavior 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 a
fuel prices, and the market’s anticipated     of randomly generated price scenarios         Cinergy 500-MW gas-fired plant on
generation level. This is considered the      that are assumed to be equally probable.      May 30, 2000, would produce a figure
holding 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 PaR
approaches, the drivers of market prices      chastic processes the prices follow, and      metric, the calculation of total profit
16                                                                                                   Global Energy Business, May/June 2001
Risk management
exposure 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 contracts
a 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 that
completely unhedged for the premium          worse, regulatory rules made it illegal      derivative prices follow geometric
summer months and selling its output on      for them to hedge their positions sub-       Brownian motion with a monthly term
a daily basis would have resulted in an      stantially.                                  structure of volatility and drift, and spot
erosion 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. The
lar its assumption that past events are      in a simulated environment and provid-       regime-switching model for spot prices
reliable indicators of the future—are        ed an accurate measure of their risk         allows for the large discontinuous
perhaps most glaring if one considers        exposure. s                                  jumps observed in electricity prices.
the California crisis. No amount of his-                                                     Figure 1 depicts a single simulation
torical data on wholesale prices could                                                    of forward and spot prices for power
have predicted what happened in the            Chal Barnwell is vice president of the     during a typical summer in North
                                               Houston operations of KWI (www.kwi.com),
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 less


Earnings at Risk:                                                                         1. Sample simulation of
                                                                                          power prices

Better for asset owners
          ccounting rules prohibit ener- the profit function. The distribution of


A         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, as
assets 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 and
their assets as quickly as companies         cal assets. A well-constructed EaR
                                                                                           outputs
with purely financial portfolios. For        model assesses the impact of alternative
                                                                                           Operational inputs                                 Total
these two reasons, energy companies          derivative trading strategies on both         Load. . . . . . . . . . . . . . . . . . . . . . . . . 130
are embracing Earnings at Risk (EaR) tails of the profit distribution curve.               Heat rate . . . . . . . . . . . . . . . . . . . 13,653
as 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 . . . 229
EaR defined                                  How might EaR be used in the ener-
                                                                                           Shutdown time, hours . . . . . . . . . . . . . . 1
                                             gy industry? Consider the hypotheti-          Ramp-up rate, MW/hr . . . . . . . . . . . . . . 3
Calculations of EaR follow calcula-          cal case of E-Corp., a U.S. firm that         Rampdown rate, MW/hr . . . . . . . . . . . 15
tions 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 . . . . . . . . . . . . . . 415
vide an estimate of the distribution of      portfolio both to that of a portfolio of      Capacity factor, % . . . . . . . . . . . . . . . . 68

Global Energy Business, May/June 2001                                                                                                              17
Risk management
volatile and turn flat during delivery. Of              Statistics:                                                  2. Distribution of net revenues
course, this is only a single simulation                EaR (C.1. 95%) 11,109,200                      100
of prices; a fair assessment of risk                    Mean           54,034,300
                                                        Maximum         74,851,500                                  90
requires several thousand simulations.
                                                        Minimum        32,599,900




                                                                                        Probability of occurrence
                                                                                                                    80
   Given a series of price simulations                  Std. dev.        7,497,690
and the operating attributes for the                                                                                70
                                                        Bin data:
plant, the dispatch of electricity as well                                                                          60
                                                        Level            Net revenue
as the corresponding revenue informa-                   0                 32,599,900                                50
tion can be simulated for the year by                   1                  37,721,800
                                                                                                                    40
mapping each hour of each day’s spot                    5                 42,925,100
price for power and natural gas to the                  10                44,797,200                                30
                                                        20                47,002,200
potential generation of electricity. Table                                                                          20
                                                        30                49,445,100
1 tabulates the operational attributes of               40                51,543,600                                10
this plant, its expected generation,                    50                53,826,900
                                                                                                                     0
capacity 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 for
effective risk management and budget-                   of owned assets, commercial contracts,                                               set the volatility of the earnings of the
ing. The rest of the data needed must                   and/or hedge positions. Suppose that in                                              power plants. When these portfolios are
come from an examination of the distri-                 addition to the merchant plant, E-Corp.                                              aggregated into one exposure, overall
bution of net revenues. Figure 2 depicts                has a strip of natural gas forward pur-                                              risk is reduced to $3.5 million, based on
the probability density function (PDF)                  chases (1 million mmBtu) and electricity                                             the offsetting profits of the plant and the
and 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 hazards
summary on the left indicates that the                  VaR or EaR of this portfolio alone with-                                             of not fully considering all assets that are
plant’s expected net revenues are esti-                 out considering the generation asset.                                                subject to market risk in risk analysis. If
mated at $54 million, and that its                      However, the generation plant has the                                                E-Corp. had measured the portfolio of
Earnings at Risk figure is $11.1 million.               potential to offset some of the risk of this                                         forward contracts in isolation, it would
In 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 to
net revenues, and it is 95% certain it                  ation plant in isolation and the portfolio                                           pursue an erroneous hedge strategy. The
will 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 (www.e-acumen.com), San
    70 . . . . . . . . 57,773,800      70 . . . . . . . . . 6,001,630      70 . . . . . . . . 57,106,300                                      Francisco.


18                                                                                                                                                    Global Energy Business, May/June 2001
Risk management
                                                                                           lish whether a company has adequate

Cash Flow at Risk                                                                          cash reserves to service its debt in the
                                                                                           event of an earnings jolt. What’s more,
                                                                                           because it can inform decision-making

for 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 data
Research 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 statistical
to forecast earnings volatility one                                                        methodology for transforming these
quarter 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 and
broad statistical analysis of earnings used Value at Risk, or VaR, to manage               year-ahead horizons based upon large
results 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 to
logical 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 financial
What and who it’s for
                                             strategy and long-term investment planning
The introduction of the C-far model has
coincided with what has become a             if they are affected by a market rever-       ments about the probability of one
very 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 allow
largest companies, including many            aggregate risk against a company’s            quantification of the probability of
utilities. 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 likelihood
most in the mind of any CFO or high-         tify the risks caused by individual           that cash flow falls by 50% in the
level 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 company
the 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-benchmarked
ceivable type of risk to which a compa-      nificant drop in earnings. As a tool for      perspective. s
ny can be exposed and produces a risk        corporate treasurers and CFOs, C-far
profile that can help answer these criti-    promises to enhance financial strategy         Louis Guth is senior vice president, and
cal questions. Its applicability goes        and long-term investment planning.             Kristina Sepetys is senior consultant (San
beyond volatile industries—such as           But the model can also be used to help         Francisco office) at National Economic
energy, manufacturing, and high-tech         companies assess their capital structure       Research Associates (www.nera.com), White
                                                                                            Plains, N.Y.
technology. Lawyers can use it to clari-     and credit-worthiness. C-far can estab-
Global Energy Business, May/June 2001                                                                                                19
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Marketrisk

  • 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 time V a number that the chief executive of any energy trading company should know before leaving the office each day. It indicates how much 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 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
  • 2. Risk management energy companies? In the second section, Chal Barn- well points out its weak- Calculating and nesses. He says that if ener- gy companies use VaR the using Value at Risk way that banks use it, they could 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 risk that his company has come up 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 LANCO called 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 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- gate those results to form a distribu- Variance-covariance duce another measure of 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 Scenario generation engine their company’s market risk exposure. 4 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 ...... –$645,334 –$243,000 $543,000 ask: “What is the probabil- ity that this year’s cash flow 160 will be inadequate to fund 140 Monte Carlo simulation VaR our strategic investments?” 120 No. of occurrences The answer takes the form 100 of a risk profile, which the 80 model generates by taking 60 into account the different 40 types of risk to which a com- 20 pany 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 million Global Energy Business, May/June 2001 13
  • 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 losses No. 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. Risk management with the risk of events that are very unlikely to occur but could lead to 3. Extreme value theory applied to value at risk 1.6 catastrophic losses. Yet traditional EV-VaR vs. normal VaR VaR calculation methods tend to ignore 1.4 Historical Cumulative probabilities, % 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 VaR based value distribution produce the largest losses. 0.6 on normal distribution Using stress tests and scenario analy- 0.4 ses to simulate the changes in the value of a portfolio under hypothetically 0.2 extreme market conditions is no solu- 0.0 tion, because they cannot explore all –35 –30 –25 –20 –15 –10 –5 0 possible scenarios. What’s more, such Loss, % analyses do not indicate how likely it is that 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 most events are not unique to risk manage- prescriptive purposes. Now, howev- other fields because electricity and gas ment; they also arise in disciplines such er, firms in dynamic industries— production and trading are physical as hydrology and structural engineer- such as energy—are beginning to use processes. ing, where extreme events can have it for more proactive purposes—for As energy markets become more devastating consequences. Researchers risk management rather than just risk complex, energy firms are embracing and 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 become theory (EVT). EVT is a specialist much risk relative to their level of more competitive. s branch of statistics that derives general return. It can also point out which properties of the tail, or extreme end, of assets, business processes, and activ- Dr. Carlos Blanco is manager of global a distribution by making the best possi- ities are increasing or decreasing the support and educational services at ble 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 (www.fea.com) Berkeley, Calif. extreme tail of a distribution, VaR can risk management in the energy industry be estimated with a confidence of greater than 95%. The difference EVT makes to VaR estimates is illustrated in Figure 3, which shows the tail of the West Texas Intermediate (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 than vertical axis represents the tail (assum- ing that logarithmic returns follow a normal distribution), and the other con- Value at Risk tinuous line represents the tail of an extreme value distribution fitted to ompanies that generate or Volume risk these data. The message this figure con- veys is that the confidence level of EVT-calculated VaRs is much higher than 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 industry Toward 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 most Traditionally, the use of VaR within variety of reasons. important of these is volume risk, Global Energy Business, May/June 2001 15
  • 5. Risk management which occurs whenever a power plant 1.Breakpoint has an unplanned outage, customers use more power than expected, or a trans- mission line fails. 150 Volume risks—and their impact on Relatively 80 Delivery price value and profit—are not caused by stable could vary forward significantly market price movements, but rather by prices 60 from the physical problems, such as those men- forward price tioned above. If anything, the converse expectation is true: Market price movements may 30 result from volume risks. In fact, the Feb 00 May 00 Sep 00 Feb 01 June 01 biggest and most shocking market price Break point movements in power markets can all be traced 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/or Companies 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 the ically rely on a “stop loss” strategy. more volatile. For energy trading firms, volatility and shape of the forward price They believe that they can quickly VaR remains a valid way to measure curves observed historically during the close out their position to limit the their risk exposure before the break- delivery period, as well as projected damage from negative market price point date. However, for many other price curves and volatility for that time movements. However, the time required energy companies—including genera- frame. The PaR metric, then, reflects to close a position depends on market tors, retail distributors, and entities the entire exposed profit of a portfolio liquidity—but liquidity changes over involved in complex marketing transac- position, not just the change expected in time 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 accurately ture power markets, price spikes can measure their risk. PaR in practice be 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 to tion 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 assuming world 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 drive breakpoints based modeling, which basically tests spot prices to abnormally high points the entire range of risks affecting spot at exactly the wrong time for the What makes assessing risk in power prices at the time of delivery. The model hedger—when the facility is down. In markets complex is that market prices follows a pseudo-Monte Carlo approach these circumstances, a VaR measure change drastically as the date of phys- that re-bases spot price simulations to based on a short holding period of two ical 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 when behavior 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 a fuel prices, and the market’s anticipated of randomly generated price scenarios Cinergy 500-MW gas-fired plant on generation level. This is considered the that are assumed to be equally probable. May 30, 2000, would produce a figure holding 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 PaR approaches, the drivers of market prices chastic processes the prices follow, and metric, the calculation of total profit 16 Global Energy Business, May/June 2001
  • 6. Risk management exposure 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 contracts a 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 that completely unhedged for the premium worse, regulatory rules made it illegal derivative prices follow geometric summer months and selling its output on for them to hedge their positions sub- Brownian motion with a monthly term a daily basis would have resulted in an stantially. structure of volatility and drift, and spot erosion 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. The lar its assumption that past events are in a simulated environment and provid- regime-switching model for spot prices reliable indicators of the future—are ed an accurate measure of their risk allows for the large discontinuous perhaps most glaring if one considers exposure. s jumps observed in electricity prices. the California crisis. No amount of his- Figure 1 depicts a single simulation torical data on wholesale prices could of forward and spot prices for power have predicted what happened in the Chal Barnwell is vice president of the during a typical summer in North Houston operations of KWI (www.kwi.com), 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 less Earnings at Risk: 1. Sample simulation of power prices Better for asset owners ccounting rules prohibit ener- the profit function. The distribution of A 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, as assets 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 and their assets as quickly as companies cal assets. A well-constructed EaR outputs with purely financial portfolios. For model assesses the impact of alternative Operational inputs Total these two reasons, energy companies derivative trading strategies on both Load. . . . . . . . . . . . . . . . . . . . . . . . . 130 are embracing Earnings at Risk (EaR) tails of the profit distribution curve. Heat rate . . . . . . . . . . . . . . . . . . . 13,653 as 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 . . . 229 EaR defined How might EaR be used in the ener- Shutdown time, hours . . . . . . . . . . . . . . 1 gy industry? Consider the hypotheti- Ramp-up rate, MW/hr . . . . . . . . . . . . . . 3 Calculations of EaR follow calcula- cal case of E-Corp., a U.S. firm that Rampdown rate, MW/hr . . . . . . . . . . . 15 tions 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 . . . . . . . . . . . . . . 415 vide an estimate of the distribution of portfolio both to that of a portfolio of Capacity factor, % . . . . . . . . . . . . . . . . 68 Global Energy Business, May/June 2001 17
  • 7. Risk management volatile and turn flat during delivery. Of Statistics: 2. Distribution of net revenues course, this is only a single simulation EaR (C.1. 95%) 11,109,200 100 of prices; a fair assessment of risk Mean 54,034,300 Maximum 74,851,500 90 requires several thousand simulations. Minimum 32,599,900 Probability of occurrence 80 Given a series of price simulations Std. dev. 7,497,690 and the operating attributes for the 70 Bin data: plant, the dispatch of electricity as well 60 Level Net revenue as the corresponding revenue informa- 0 32,599,900 50 tion can be simulated for the year by 1 37,721,800 40 mapping each hour of each day’s spot 5 42,925,100 price for power and natural gas to the 10 44,797,200 30 20 47,002,200 potential generation of electricity. Table 20 30 49,445,100 1 tabulates the operational attributes of 40 51,543,600 10 this plant, its expected generation, 50 53,826,900 0 capacity 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 for effective risk management and budget- of owned assets, commercial contracts, set the volatility of the earnings of the ing. The rest of the data needed must and/or hedge positions. Suppose that in power plants. When these portfolios are come from an examination of the distri- addition to the merchant plant, E-Corp. aggregated into one exposure, overall bution of net revenues. Figure 2 depicts has a strip of natural gas forward pur- risk is reduced to $3.5 million, based on the probability density function (PDF) chases (1 million mmBtu) and electricity the offsetting profits of the plant and the and 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 hazards summary on the left indicates that the VaR or EaR of this portfolio alone with- of not fully considering all assets that are plant’s expected net revenues are esti- out considering the generation asset. subject to market risk in risk analysis. If mated at $54 million, and that its However, the generation plant has the E-Corp. had measured the portfolio of Earnings at Risk figure is $11.1 million. potential to offset some of the risk of this forward contracts in isolation, it would In 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 to net revenues, and it is 95% certain it ation plant in isolation and the portfolio pursue an erroneous hedge strategy. The will 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 (www.e-acumen.com), San 70 . . . . . . . . 57,773,800 70 . . . . . . . . . 6,001,630 70 . . . . . . . . 57,106,300 Francisco. 18 Global Energy Business, May/June 2001
  • 8. Risk management lish whether a company has adequate Cash Flow at Risk cash reserves to service its debt in the event of an earnings jolt. What’s more, because it can inform decision-making for 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 data Research 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 statistical to forecast earnings volatility one methodology for transforming these quarter 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 and broad statistical analysis of earnings used Value at Risk, or VaR, to manage year-ahead horizons based upon large results 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 to logical 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 financial What and who it’s for strategy and long-term investment planning The introduction of the C-far model has coincided with what has become a if they are affected by a market rever- ments about the probability of one very 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 allow largest companies, including many aggregate risk against a company’s quantification of the probability of utilities. 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 likelihood most in the mind of any CFO or high- tify the risks caused by individual that cash flow falls by 50% in the level 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 company the 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-benchmarked ceivable type of risk to which a compa- nificant drop in earnings. As a tool for perspective. s ny can be exposed and produces a risk corporate treasurers and CFOs, C-far profile that can help answer these criti- promises to enhance financial strategy Louis Guth is senior vice president, and cal questions. Its applicability goes and long-term investment planning. Kristina Sepetys is senior consultant (San beyond volatile industries—such as But the model can also be used to help Francisco office) at National Economic energy, manufacturing, and high-tech companies assess their capital structure Research Associates (www.nera.com), White Plains, N.Y. technology. Lawyers can use it to clari- and credit-worthiness. C-far can estab- Global Energy Business, May/June 2001 19
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