Using CAViaR models with implied volatility for value-at-risk estimation
Jeon, Jooyoung and Taylor, James (2013) Using CAViaR models with implied volatility for value-at-risk estimation. Journal of Forecasting, 32 (1). 62–74. ISSN 0277-6693 (https://doi.org/10.1002/for.1251)
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Abstract
This paper proposes 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 models, a 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 residual. 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&P500 daily returns.
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Item type: Article ID code: 39598 Dates: DateEventJanuary 2013Published27 October 2011Published OnlineSubjects: Social Sciences > Industries. Land use. Labor > Risk Management Department: Strathclyde Business School > Management Science Depositing user: Pure Administrator Date deposited: 08 May 2012 14:24 Last modified: 03 Dec 2024 01:12 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/39598