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Evaluating forecast performances of the quantile autoregression models in the present global crisis in international equity markets

Listed author(s):
  • Qing Xu
  • Terry Childs
Registered author(s):

    In this research, we compare the one-step-ahead out-of-sample forecast performances of the linear Quantile Autoregression (QAR) model as well as the latest sophisticated nonlinear copula-based QAR models for four daily equity index returns during the current financial tumultuous period. In addition, two Conditional Autoregressive Value-at-Risk (CAViaR) models proposed by Engle and Manganelli (2004) are also considered. In order to obtain the robust evaluation results, we estimate the time-varying parameters via two forecasting schemes (recursive and rolling) and examine the accuracy of the Value-at-Risk (VaR) forecast by three different test procedures. Our main findings are that the CAViaR models provide good forecast performance in most cases and they are superior to both linear and nonlinear copula-based QAR models.

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    Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

    Volume (Year): 23 (2013)
    Issue (Month): 2 (January)
    Pages: 105-117

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    Handle: RePEc:taf:apfiec:v:23:y:2013:i:2:p:105-117
    DOI: 10.1080/09603107.2012.709601
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