文章基本信息

标题：Modelling daily value-at-risk using realized volatility, non-linear support vector machine and ARCH type models
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作者：Md. Ashraful Islam Khan
期刊名称：Journal of Economics and International Finance
电子版ISSN：2006-9812
出版年度：2011
卷号：3
期号：5
页码：305-321
语种：English
出版社：Academic Journals
摘要：The aim of this paper is to compare the performance of the daily nonlinear support vector machines, the new semi-parametric tool for regression estimation, heterogeneous autoregressive (SVM-HAR)-ARCH type models based on the daily realized volatility (which uses intraday returns) with the performance of the classical HAR-ARCH type models by using different innovation distribution when the one-day ahead value-at-risk (VaR) is to be computed. The daily realized volatility is calculated using 5-, 15-min and optimally sampled intraday returns for Nikkei 225 index. This paper shows that the particular hybrid SVM-HAR-ARCH type model provides better performance when 15-min intraday returns are used. This paper also shows that the models based on a long memory skewed student distribution provide the better performance of one-day ahead value-at-risk forecasts.
关键词：Value-at-risk;HAR-RV model;nonlinear support vector machine-HAR-RV model;ARCH type models;Skewed student distribution;high frequency Nikkei 225 data