This document discusses various risk measurement methodologies used in energy trading, with a focus on Value at Risk (VaR). It provides the following key points: 1. VaR measures the worst expected loss of a portfolio over a given time period under normal market conditions. It is commonly used in energy trading but has limitations. 2. New risk measures have been developed based on VaR, including Profit at Risk (PaR), Earnings at Risk (EaR), and Cash Flow at Risk (C-far). These aim to address some weaknesses of VaR. 3. The document describes various techniques for calculating VaR, including variance-covariance, Monte Carlo simulation, and historical simulation,

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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