Volatility measures and Value-at-Risk

Dennis Bams, Gildas Blanchard and Thorsten Lehnert

International Journal of Forecasting, 2017, vol. 33, issue 4, 848-863

Abstract: We evaluate and compare the abilities of the implied volatility and historical volatility models to provide accurate Value-at-Risk forecasts. Our empirical tests on the S&P 500, Dow Jones Industrial Average and Nasdaq 100 indices over long time series of more than 20 years of daily data indicate that an implied volatility based Value-at-Risk cannot beat, and tends to be outperformed by, a simple GJR-GARCH based Value-at-Risk. This finding is robust to the use of the likelihood ratio, the dynamic quantile test or a statistical loss function for evaluating the Value-at-Risk performance.

Keywords: Value-at-Risk; Option implied volatility; Volatility risk premium; Time-series; GARCH models (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:4:p:848-863

DOI: 10.1016/j.ijforecast.2017.04.004

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