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|Title:||Diagnostics for conditional heteroscedasticity models: Some simulation results||Authors:||Tsui, A.K.||Keywords:||GARCH models
|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|
|Appears in Collections:||Staff Publications|
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