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

Dilip Kumar

No 3205528, Proceedings of Economics and Finance Conferences from International Institute of Social and Economic Sciences

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.

Keywords: Extreme value volatility estimator; Value-at-risk; Skewed Student t distribution; Risk management. (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2016-03
New Economics Papers: this item is included in nep-ecm, nep-for, nep-ore and nep-rmg
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Published in Proceedings of the Proceedings of the 5th Economic & Finance Conference, Miami, Mar 2016, pages 192-208

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Persistent link: https://EconPapers.repec.org/RePEc:sek:iefpro:3205528

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