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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

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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.

    Item type: Article
    ID code: 39598
    Keywords: value at risk, CAViaR, implied volatility, quantile regression, combining, Risk Management, Modelling and Simulation, Strategy and Management, Management Science and Operations Research, Statistics, Probability and Uncertainty, Computer Science Applications
    Subjects: Social Sciences > Industries. Land use. Labor > Risk Management
    Department: Strathclyde Business School > Management Science
    Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 08 May 2012 15:24
    Last modified: 06 Sep 2014 13:37
    URI: http://strathprints.strath.ac.uk/id/eprint/39598

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