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

Ihsan Badshah ()

International Journal of Financial Markets and Derivatives, 2015, vol. 4, issue 3/4, 213-230

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. (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=73468 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijfmkd:v:4:y:2015:i:3/4:p:213-230

Access Statistics for this article

More articles in International Journal of Financial Markets and Derivatives from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().