Using CAViaR Models with Implied Volatility for Value-at-Risk Estimation

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This paper proposes value-at risk (VaR) estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market's expectation of risk. Forecast-combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR modelsa procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residuals. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P 500 daily returns. Copyright (c) 2012 John Wiley & Sons, Ltd.
Publisher
WILEY-BLACKWELL
Issue Date
2013-01
Language
English
Article Type
Article
Citation

JOURNAL OF FORECASTING, v.32, no.1, pp.62 - 74

ISSN
0277-6693
DOI
10.1002/for.1251
URI
http://hdl.handle.net/10203/270749
Appears in Collection
RIMS Journal Papers
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