Specification tests for GARCH processes

TL;DR

This paper introduces new specification tests for GARCH models' conditional variance functions, utilizing a bootstrap method to handle boundary parameter issues, with demonstrated effectiveness through simulations and real data examples.

Contribution

It develops a novel bootstrap-based testing procedure for GARCH models that remains valid even when parameters are on the boundary of the parameter space.

Findings

Tests have excellent finite sample performance.

Bootstrap method effectively handles nuisance parameters.

Provides a useful alternative to existing Ljung-Box tests.

Abstract

This paper develops tests for the correct specification of the conditional variance function in GARCH models when the true parameter may lie on the boundary of the parameter space. The test statistics considered are of Kolmogorov-Smirnov and Cram\'{e}r-von Mises type, and are based on a certain empirical process marked by centered squared residuals. The limiting distributions of the test statistics are not free from (unknown) nuisance parameters, and hence critical values cannot be tabulated. A novel bootstrap procedure is proposed to implement the tests; it is shown to be asymptotically valid under general conditions, irrespective of the presence of nuisance parameters on the boundary. The proposed bootstrap approach is based on shrinking of the parameter estimates used to generate the bootstrap sample toward the boundary of the parameter space at a proper rate. It is simple to implement and fast in applications, as the associated test statistics have simple closed form expressions. A simulation study demonstrates that the new tests: (i) have excellent finite sample behavior in terms of empirical rejection probabilities under the null as well as under the alternative; (ii) provide a useful complement to existing procedures based on Ljung-Box type approaches. Two data examples are considered to illustrate the tests.