An uncorrelated time series can still be serially dependent due to a dynamic conditional variance process. A time series exhibiting conditional heteroscedasticity—or autocorrelation in the squared series—is said to have autoregressive conditional heteroscedastic (ARCH) effects. Engle’s ARCH test is a Lagrange multiplier test to assess the significance of ARCH effects .
Consider a time series
Suppose the innovations are generated as
Let Ht denote the history of the process available at time t. The conditional variance of yt is
Define the residual series
The alternative hypothesis for Engle’s ARCH test is autocorrelation in the squared residuals, given by the regression
To conduct Engle’s ARCH test using
you need to specify the lag m in the alternative
hypothesis. One way to choose m is to compare loglikelihood
values for different choices of m. You can use
the likelihood ratio test (
lratiotest) or information
aicbic) to compare loglikelihood values.
To generalize to a GARCH alternative, note that a GARCH(P,Q) model is locally equivalent to an ARCH(P + Q) model. This suggests also considering values m = P + Q for reasonable choices of P and Q.
The test statistic for Engle’s ARCH test is the usual F statistic for the regression on the squared residuals. Under the null hypothesis, the F statistic follows a distribution with m degrees of freedom. A large critical value indicates rejection of the null hypothesis in favor of the alternative.
As an alternative to Engle’s ARCH test, you can check
for serial dependence (ARCH effects) in a residual series by conducting
a Ljung-Box Q-test on the first m lags of the squared
residual series with
lbqtest. Similarly, you can
explore the sample autocorrelation and partial autocorrelation functions
of the squared residual series for evidence of significant autocorrelation.
 Engle, Robert F. “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica. Vol. 50, 1982, pp. 987–1007.