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Estimating and forecasting value-at-risk using the unbiased extreme value volatility estimator

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  • Dilip Kumar

    (Indian Institute of Management Kashipur)

Abstract

We provide a framework based on the unbiased extreme value volatility estimator (Namely, the AddRS estimator) to compute and predict the long position and a short position VaR, henceforth referred to as the ARFIMA-AddRS-SKST model. We evaluate its VaR forecasting performance using the unconditional coverage test and the conditional coverage test for long and short positions on four global indices (S&P 500, CAC 40, IBOVESPA and S&P CNX Nifty) and compare the results with that of a bunch of alternative models. Our findings indicate that the ARFIMA-AddRS-SKST model outperforms the alternative models in predicting the long and short position VaR. Finally, we examine the economic significance of the proposed framework in estimating and predicting VaR using Lopez loss function approach so as to identify the best model that provides the least monetary loss. Our findings indicate that the VaR forecasts based on the ARFIMA-AddRS-SKST model provides the least total loss for various x% long and short positions VaR and this supports the superior properties of the proposed framework in forecasting VaR more accurately.

Suggested Citation

  • Dilip Kumar, 2016. "Estimating and forecasting value-at-risk using the unbiased extreme value volatility estimator," Proceedings of Economics and Finance Conferences 3205528, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iefpro:3205528
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    More about this item

    Keywords

    Extreme value volatility estimator; Value-at-risk; Skewed Student t distribution; Risk management.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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