Risk Quantification in Rapidly Changing Business
Environments
Rapid changes in business have made planning more difficult and risk
management more important. Whereas business planning is based on
forecasts of expected outcomes, risk management is focused on
deviations from expected outcomes. Learn how GARCH models provide
a significant advancement in the ability to forecast potential deviations
by characterizing volatility as an evolving process.
by Samir Shah
Tillinghast–Towers Perrin
In today's growing Internet-based economy, managers are keenly aware that it's no
longer business as usual. Rapid technological change is reshaping the business
landscape and emerging new businesses and industries are redefining business
models and the fundamentals of competition. The rapid pace of change in the
business environment has increased the uncertainty in the outcomes of
management decisions and placed a greater emphasis on risk management.
Assessing that uncertainty using both qualitative and quantitative techniques is the
fundamental objective of risk analysis. This article discusses techniques for
quantifying uncertainty and presents a recent advancement called GARCH
modeling.
Uncertainty is measured in terms of the volatility of financial and operational
metrics, such as share price, interest rates, foreign exchange rates, sales volume,
commodity prices, labor cost, employee turnover, etc. Within the scope of this
discussion, volatility is simply annualized variance, which is a measure of the
deviation of performance from expected values. Rapid changes in business activities
manifest themselves in deviations of financial and operational performance from
expectations, thus increasing volatility. The task of any volatility model is to
describe the typical historical pattern of volatility and use this to forecast future
episodes. The more reliable the forecast of volatility, the easier it is to protect
against surprises by explicitly reflecting volatility forecasts in management decision
making.
Current Methods of Modeling Volatility
Implicit in the definition of "volatility" is an expectation of future outcome.
Expectations of future outcomes are based on forecasts of financial and operational
metrics, which in turn are derived from extrapolation of historical trends. This
typically involves developing a mathematical regression or time series model that
best represents historical movements. Historical volatility is then determined by
measuring the deviations around the fit, i.e., the residuals represented by the error
term in the regression model.
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In order to quantify risk, forecasting models must explicitly reflect the uncertainty in
the forecast. Typically, a stochastic differential equation (SDE) is used to generate
hundreds or thousands of alternative scenarios that are then summarized into
probability distributions. (See the previous article in this series, Sailing through
Rough Winds of Macroeconomic Risks, for a description of SDEs.) An SDE has two
parts: one part to forecast the expected outcome and another to forecast the
variance around the expected outcome. The forecast of expected outcome is
developed by fitting a regression or time series model to historical data. The
variance forecast is typically modeled as a constant value equal to the historical
variance around the fitted regression model.
For example, a forecast for weekly sales volume could be derived from historical
data shown in Figure 1. A naïve forecast of sales would be to use the average of
historical sales volume. The historical volatility would then be measured in terms of
the historical deviations around the average. A more enlightened forecast would be
to develop a regression model that reflects both the seasonal fluctuations and the
long-term increasing trend (Figure 2). It's clear from a comparison of the forecasts
that because the regression model better represents historical movements in
weekly sales volume, the historical volatility is much less than under the naïve
model.
Figure 1
Weekly Sales Chart
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The historical average is used as the forecast resulting in significant
uncertainty in the forecast.
Figure 2
Weekly Sales Chart
A regression model reflecting how the trend and seasonality are used to
develop the forecast, resulting in a decrease in the uncertainty of the
forecast.
It should be apparent from this example that a forecast of the volatility depends on
the reliability of the model used to develop expected outcomes. Naturally, the better
our understanding of the drivers of historical fluctuations of sales volume, the less
uncertainty (risk) there will be in our forecast. Investment in time and effort to
develop a good model of expected performance pays off in reducing risk and the
cost of risk management, and increasing the reliability of the risk management plan.
A fundamental drawback of this approach however, is the assumption that volatility
will be constant in the future. Recall that volatility is variance over 1 year. If
volatility is constant, then for example, the variance of a 2-year forecast is twice the
variance of a 1-year forecast. Generalizing this means that the variance in the
forecast increases at a constant rate as the length of the forecast time period
increases. The standard deviation, which is the square root of the variance,
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therefore increases as a square root of the length of the forecast time period. This
explains the shape of the uncertainty in the forecast in Figure 1, which resembles a
square root function. The same is true in Figure 2 after adjusting for the seasonality.
Often, however, constant volatility is not a valid assumption. Financial markets in
particular have exhibited changes in volatility over time. A rapid succession of
changes in business environment—as evident in the Internet age—result in rapid
changes in financial and operational performance. These bursts of activity result in
volatility clusters, i.e., successive periods of high volatility and low volatility. In
other words, volatility is not constant! In these instances, approaches based on
constant volatility can result in poor performance of the risk management plan.
Modeling Volatility Using GARCH Models
As mentioned previously, the part of the SDE used to forecast variance is typically
expressed as a constant. Of course, it doesn't have to be expressed this way. Like
the forecast of expected outcome, the variance forecast can also be expressed more
fully as a regression or time series model. This is in fact what Robert Engle,
professor of Economics at University of California, San Diego, did when he
developed an ARCH model in 1982.
ARCH stands for Autoregressive Conditional Heteroscedasticity. "Autoregressive"
means that the volatility at each point in time is expressed in terms of volatility in
prior periods, i.e., the independent variable in the regression model is volatility in
prior periods. "Conditional Heteroscedasticity" means changing variance, or
"volatility clustering." Tim Bollerslev, who was Engle's graduate student, later
developed a generalized version of ARCH called, appropriately, Generalized
Autoregressive Conditional Heteroscedasticity (GARCH), in 1986. Although the
approach was developed more than 15 years ago, it has become widely used in the
financial markets only recently as practitioners have become more adept in the
underlying mathematics.
How GARCH Works
First, the forecast of expected outcome is determined as usual by fitting a
regression model to historical data. Let's call this the Model of Expected Outcome.
This model will explain much—but not all—of the historical fluctuations depending
on the "goodness of fit."
Next, another regression is performed to model historical variance. Let's call this the
Model of Expected Variance. It has the following form:
σ2t = c + aε2t-1 + bσ2t-1
where:
σ2t is the variance at time t,
ε2t-1 is the square of the difference between the actual historical value at time t-1
and the value based on the Model of Expected Outcome,
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a, b, and c are parameters that will provide the best fit of the regression model to
historical data. The parameters are constrained such that a, b, and c > 0 and a + b
< 1.
Once the model parameters (a, b, and c) are determined, then, after much math,
the following equation can be derived for forecasting volatility:
σ2t+s = c + (a + b)s (σ2t - c)
where σ2t+s is the forecast of the expected variance at some future time t+s. In this
forecast, the volatility changes over time and the variance of the forecast does not
increase linearly with the forecast time period.
Notice that GARCH produces two forecasts: one for the expected outcome and one
for the variance around the expected outcome. Let's examine the fundamental
characteristics of the variance forecast.
Variance Forecast
The variance forecast has the property of mean reversion when c > 0 (as we have
constrained it to be). This means that although volatility is not constant, there is a
long-term constant level of volatility around which there are short-term fluctuations.
The value of c is the mean long-term volatility level. At the time of the forecast, the
volatility may be either greater or less than this long-term level.
The forecast will start at the current level and revert to its long-term level over time.
The rate at which it will revert is based on the values of the parameters a and b. The
sum of a and b will be between 0 and 1 due to the constraints imposed in the
regression. The closer the sum is to 0, the faster the volatility will shrug off a
short-term period of extraordinarily high or low volatility to return to its long-term
level. If the sum is closer to 1, it will revert more slowly. Thus volatility forecasts
using GARCH have a term structure as shown in Figure 3.
Note that this mean reversion model captures the volatility clustering behavior of
some markets discussed above. When volatility is higher than its long-term level,
the forecast also generates higher variance forecasts until the effect gradually
dissipates. Of course, the same characteristic exists also when volatility is lower
than its long-term level.
GARCH models are commonly used in the financial markets to value derivatives
whose prices are sensitive to volatility forecasts. Improvements in volatility
forecasting have allowed many in fact to trade volatility itself rather than the
underlying asset. The success of GARCH modeling in the financial markets suggests
opportunities to apply the approach to other areas of corporate financial and
operational risk management.
Figure 3
Volatility Term Structure Chart
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The forecast of volatility either decreases or increases over the forecast
period, depending on whether the current period is experiencing higher or
lower volatility, respectively, than the long-term level.
Conclusion
Volatility is a fundamental concept in managing risk. Whereas business planning is
based on forecasts of expected outcomes, risk management is focused on
deviations from expected outcomes. GARCH provides a significant advancement in
our ability to forecast the range of potential deviations from business plans by
characterizing volatility as a process that evolves over time rather than as a
constant parameter.
The rapid changes in the business environment have made business planning more
difficult and risk management more important. Indeed, some companies have
reorganized themselves to elevate risk management to the same level as business
planning—creation of a new Chief Risk Officer (CRO) position, for example. GARCH
models provide a valuable tool for CROs and other senior managers to reliably
forecast risk in order to develop effective risk management plans.
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