Residual-Based Diagnostics for Conditional Heteroscedasticity Models

By Y. Tse. 2002

112 citations recorded by the source corpus.

Summary We examine the residual-based diagnostics for univariate and multivariate conditional heteroscedasticity models. The tests are based on the parameter estimates of an autoregression with the squared standardized residuals or the cross products of the standardized residuals as dependent variables. As the regression involves estimated regressors the standard distribution theories of the ordinary least squares estimates do not apply. We provide the asymptotic variance of the regression estimates. Diagnostic statistics, which are asymptotically distributed as χ2, are constructed. A Monte Carlo experiment is conducted to investigate the finite-sample properties of the residual-based tests for both univariate and multivariate models. The results show that the residual-based diagnostics provide useful checks for model adequacy in both univariate and multivariate cases.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1ea29812f956fd1822970c700ca34adc4d310f94

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Citations in the corpus

Oil and stock returns: Evidence from European industrial sector indices in a time-varying environment (2013, unknown): “…evidence to support our model specification have been conducted, i.e. multivariate Q-statistic for residual serial correlation (Lütkepohl, 1991), testing for presence of ARCH effects in the residuals 9 ; see for example Hacker and Abdulnasser (2005), Tse (2002) and Xekalaki and Degiannakis (2010).”
A Multivariate Generalized Orthogonal Factor GARCH Model (2007, background): “The tests of Tse (2002) revealed no predictability of the squares or cross-products of standardized residuals by their lagged values at reasonable significance levels.”
GARCH Models (2010, unknown): “Portmanteau tests on the normalized residuals of multivariate GARCH processes were proposed, in particular, by Tse (2002), Duchesne and Lalancette (2003).”
A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors (2001, background): “Tse (1999) provides a recent review of this literature.”
Modeling and forecasting the volatility of petroleum futures prices (2013, background): “6 Tse (2002) developed residual-based diagnostics (RBD) for conditional heteroscedasticity to test the n ull hypothesis of a correct model specification.”