GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics
he great workhorse of appliedeconometrics is the least squares model. This is a natural choice, because applied econometricians are typically called upon to determine how much one variable will change in response to a change insome other variable. Increasingly however, econometricians are being asked to forecast and analyze the size of the errors of the model. In this case, the questions are about volatility, and the standardtools have become the ARCH/ GARCH models. The basic version of the least squares model assumes that the expected value of all error terms, when squared, is the same at any given point. This assumptionis called homoskedasticity, and it is this assumption that is the focus of ARCH/ GARCH models. Data in which the variances of the error terms are not equal, in which the error terms may reasonably beexpected to be larger for some points or ranges of the data than for others, are said to suffer from heteroskedasticity. The standard warning is that in the presence of heteroskedasticity, theregression coefﬁcients for an ordinary least squares regression are still unbiased, but the standard errors and conﬁdence intervals estimated by conventional procedures will be too narrow, giving a falsesense of precision. Instead of considering this as a problem to be corrected, ARCH and GARCH models treat heteroskedasticity as a variance to be modeled. As a result, not only are the deﬁciencies ofleast squares corrected, but a prediction is computed for the variance of each error term. This prediction turns out often to be of interest, particularly in applications in ﬁnance. The warnings aboutheteroskedasticity have usually been applied only to cross-section models, not to time series models. For example, if one looked at the
y Robert Engle is the Michael Armellino Professor of Finance,... [continua]
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