Diagnostics for conditional heteroscedasticity models: some simulation results

Albert Tsui

Mathematics and Computers in Simulation (MATCOM), 2004, vol. 64, issue 1, 113-119

Abstract: In this paper, we study the size and power of various diagnostic statistics for univariate conditional heteroscedasticity models. These test statistics include the residual-based tests recently derived by Tse, Li and Mak, and Wooldridge, respectively. Monte-Carlo experiments with 1000 replications are conducted to generate conditional variances which follow the autoregressive conditional heteroscedasticity (ARCH)/GARCH processes. We use quasi-maximum likelihood estimation (MLE) method to obtain estimates of parameters under different ARCH/ generalized ARCH (GARCH) models. It is found that the Tse and Li–Mak diagnostics are more powerful.

Keywords: GARCH models; Residual-based diagnostics; Simulation (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:64:y:2004:i:1:p:113-119

DOI: 10.1016/S0378-4754(03)00125-3

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