22.1Measuring Risk Exposure

Exposures are measured in a variety of ways, but as Chapter 21 and the discussion

above noted, one can generally view an exposure as a factor beta, similar to the factor

betas discussed in Chapter 6. This section discusses the measurement of risk exposure

with factor models.

Assume that currency and interest rate uncertainty contribute to a firm’s risk of

doing business in Japan. The firm can measure the exposure of a future cash flow to

these two factors by estimating a factor model. Assume that the estimation of the fac-

tor model generates the equation

1

As an alternative to cash flows, firms also focus on hedging the risk of their future earnings.

²While the analysis in Chapter 21 did not indicate that firms should minimize risk exposure, this

chapter, for simplicity of exposition, focuses on how to implement hedging that minimizes exposure to

risk factors. Our results can easily be generalized to target any desired set of risk exposures.

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Markets and Corporate			Companies, 2002
Strategy, Second Edition

Chapter 22

The Practice of Hedging

775

˜302˜4F˜˜

CF

currint

where

˜ cash flow (in $ millions)

C

˜percentage change in the ¥/US$ exchange rate over the coming year

F

curr

˜percentage change in the short-term interest rate over the coming year

F

int

The 2 and 4 in the equation are the sensitivities of the cash flow to the exchange rate

and interest rates, respectively, or the cash flow’s factor betas.The ˜term represents

the risks of the cash flow not captured by these two risk factors.

Using Regression to Estimate the Risk Exposure

The regression method, one of the most popular tools for analyzing risk and devel-

oping hedges, examines how the unhedged cash flows of the firm performed histori-

cally in relation to a risk factor. Specifically, it estimates the factor betas as slope

coefficients from regressions of historical returns or cash flows on the risk factors.

Measuring Risk Exposure with Simulations

The simulation methodis a forward-looking method of estimating risk exposure. In

rapidly changing industries, the simulation method is superior to regression estimation

using historical data, which is backward looking.

Implementation with Scenarios.To implement the simulation method, a manager

needs to forecast earnings or cash flows for a variety of factor realizations. With

exchange rate risk, for example, a manager would implement this method by specify-

ing a wide range of different exchange rate scenarios. Each scenario includes an esti-

mate of the profits or cash flows that would occur under a variety of assumptions about

industry demand and about competitor and supplier responses.

Simulation versus Regression.Although simulations require much more judgment on

the part of the analyst, they do not require that the past history of the firm provide the best

estimate of the future. In reality, this can be very important in a changing environment.

For example, one would not want to derive an estimate of General Motors’(GM’s) yen

exposure solely from regressions that make use of data from the past 10 years. Competi-

tion from Japanese automakers has changed considerably over this historical period and it

is unlikely that a firm’s future exposure to the yen will be the same as its past exposure.

Modifying Initial Estimates Obtained from Regression.The simulation method for

exchange rate risk simply asks the manager to estimate the firm’s future costs and rev-

enues under different exchange rate scenarios to obtain profit (or cash flow). Regression

may provide useful inputs for these estimates, but the manager is not limited to the regres-

sion results for these estimates. He or she also could incorporate assumptions about the

sensitivity of the demand for a product to its price as well as expected competitor

responses to exchange rate changes. In addition, the regression analysis specifies a linear

relation between the determinants of profits (or cash flows) and exchange rate changes

which is unlikely to be true in reality. The manager will want to modify the regression-

based estimates to account for any nonlinearities in the statistical relationships.

For example, General Motors might assume that Japanese automakers will main-

tain the same U.S. dollar prices for their cars when faced with a small increase in the

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Markets and Corporate			Companies, 2002
Strategy, Second Edition

776Part VIRisk Management

value of the yen, giving up some profit to maintain market share. If this is the case,

GM would not benefit from a small increase in the value of the yen. However, if the

yen strengthens significantly, Japanese automakers may find it preferable to abandon

certain U.S. markets, an action that would greatly benefit General Motors.

Similar arguments can be made about a weakening of the yen, which provides an

advantage to the Japanese automakers. Aslight weakening may have no effect on the

prices U.S. dealers pay for Japanese cars, perhaps because Japanese automakers are

concerned about having to later raise prices if the yen subsequently strengthens.

However, if the yen weakens considerably, giving Japanese automakers a large cost

advantage, they might exploit the opportunity to expand market share, which would

significantly reduce General Motors’profits.

Prespecification of Factor Betas from Theoretical Relations

In some cases, factor betas can be prespecified using knowledge of theory. For exam-

ple, the commitments of Metallgesellschaft to sell heating oil at predetermined prices

can be viewed as forward contracts. If the risk factor is the price of oil in 10 years, the

factor beta of a 10-year forward contract for such a risk factor must equal 1.