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Value-at-Risk prediction using option-implied risk measures

Kai Schindelhauer and Chen Zhou

DNB Working Papers from Netherlands Central Bank, Research Department

Abstract: This paper investigates the prediction of Value-at-Risk (VaR) using option-implied information obtained by the maximum entropy method. The maximum entropy method provides an estimate of the risk-neutral distribution based on option prices. Besides commonly used implied volatility, we obtain implied skewness, kurtosis and quantile from the estimated risk-neutral distribution. We find that using the implied volatility and implied quantile as explanatory variables significantly outperforms considered benchmarks in predicting the VaR, including the commonly used GARCH(1,1)-model. This holds for all considered VaR prediction models and VaR probability levels. Overall, a simple quantile regression model performs best for all considered VaR probability levels and forecast horizons.

Keywords: Implied Quantile; GARCH; Quantile Regression; Comparative Backtest (search for similar items in EconPapers)
JEL-codes: C14 G17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban, nep-ore and nep-rmg
Date: 2018-10
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