Title: The information content of the VDAX volatility index and backtesting daily value-at-risk models

Authors: Ihsan Ullah Badshah

Addresses: Department of Finance, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand

Abstract: This paper examines the information content of the new VDAX volatility index to forecast daily value-at-risk (VaR) estimates and compares its VaR forecasts with the VaR forecasts of the asymmetric GARCH model of Glosten, Jagannathan and Runkle (GJR) (1993) and RiskMetrics model. The performance of the daily VaR models is evaluated both in-sample and out-of-sample using unconditional coverage, independence, and conditional coverage tests. We find that the information content of implied volatility is superior to that of historical volatility for the daily VaR forecasts of a portfolio of the DAX 30 stock index. Backtesting results suggest the following rank for our VaR models, from best to worst, GJR-GARCH(1, 1) augmented with implied volatility, implied volatility, GJR-GARCH(1, 1), and RiskMetrics. Our findings have implications for traders, risk managers and regulators.

Keywords: backtesting; GARCH; Germany; DAX; stock markets; implied volatility; value-at-risk; information content; VDAX volatility index; daily VAR models; historical volatility; financial risk management.

DOI: 10.1504/IJFMD.2015.073468

International Journal of Financial Markets and Derivatives, 2015 Vol.4 No.3/4, pp.213 - 230

Received: 18 Nov 2014
Accepted: 30 Jun 2015
Published online: 09 Dec 2015 *

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