Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0378-4754(03)00125-3
Title: Diagnostics for conditional heteroscedasticity models: Some simulation results
Authors: Tsui, A.K. 
Keywords: GARCH models
Residual-based diagnostics
Simulation
Issue Date: 2004
Citation: Tsui, A.K. (2004). Diagnostics for conditional heteroscedasticity models: Some simulation results. Mathematics and Computers in Simulation 64 (1) : 113-119. ScholarBank@NUS Repository. https://doi.org/10.1016/S0378-4754(03)00125-3
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.©2003 IMACS. Published by Elsevier B.V. All rights reserved.
Source Title: Mathematics and Computers in Simulation
URI: http://scholarbank.nus.edu.sg/handle/10635/19943
ISSN: 03784754
DOI: 10.1016/S0378-4754(03)00125-3
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