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首页> 外文期刊>Applied financial economics >Evaluating forecast performances of the quantile autoregression models in the present global crisis in international equity markets
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Evaluating forecast performances of the quantile autoregression models in the present global crisis in international equity markets

机译：在国际股票市场当前的全球危机中评估分位数自回归模型的预测性能

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摘要

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.
机译：在这项研究中，我们比较了线性分位数自回归（QAR）模型以及最新复杂的基于非线性copula的QAR模型对当前财务期间四个日股指收益率的一步一步的样本外预测性能动荡时期。此外，还考虑了Engle和Manganelli（2004）提出的两个条件自回归风险价值（CAViaR）模型。为了获得可靠的评估结果，我们通过两种预测方案（递归和滚动）来估计时变参数，并通过三种不同的测试程序来检验风险价值（VaR）预测的准确性。我们的主要发现是，在大多数情况下，CAViaR模型可提供良好的预测性能，并且它们优于基于线性和非线性copula的QAR模型。

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