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The Role of High Frequency Intra-Daily Data, Daily Range and Implied Volatility in Multi-Period Value-at-Risk Forecasting
Year Of Publication: 2011
Month Of Publication: December
Pages: 39
Download Count: 8
View Count: 1862
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 12-16-2011
Publisher: Administrator
In this paper, we assess the informational content of daily range, realized variance, realized bipower variation, two time scale realized variance, realized range and implied volatility in daily, weekly, biweekly and monthly out-of-sample Value-at-Risk (VaR) predictions. We use the recently proposed Realized GARCH model combined with the skewed student distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi-period VaR estimates. The VaR forecasts are evaluated in terms of statistical and regulatory accuracy as well as capital efficiency. Our empirical findings, based on the S&P 500 stock index, indicate that almost all realized and implied volatility measures can produce statistically and regulatory precise VaR forecasts across forecasting horizons, with the implied volatility being especially accurate in monthly VaR forecasts.
This document is published in Journal of Forecasting.
Louzis, Dimitrios P. Sign in to follow this author
Xanthopulos-Sisinis, Spyros Sign in to follow this author
Refenes, Apostolos-Paul N. Sign in to follow this author
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