A Multivariate Fgd Technique to Improve VAR Computation in Equity Markets
30 Pages Posted: 15 Jan 2003
Date Written: September 2002
Abstract
We present a multivariate, non-parametric technique for constructing reliable daily VaR predictions for individual assets belonging to a common equity market segment, which takes also into account the possible dependence structure between the assets and is still computationally feasible in large dimensions. The procedure is based on functional gradient descent (FGD) estimation for the volatility matrix (Audrino and Bühlmann, 2002) in connection with asset historical simulation and can also be seen as a multivariate extension of the filtered historical simulation method proposed by Barone-Adesi et al. (1998). Our FGD algorithm is very general and can be further adapted to other multivariate problems dealing with (volatility) function estimation. We concentrate our empirical investigations on the Swiss pharmaceutical and the US biotechnological equity market and we collect, using statistical and economical backtests, strong empirical evidence of the better predictive potential of our multivariate strategy over other univariate techniques, with a resulting significant improvement in the measurement of risk.
Keywords: Volatility estimation, Filtered Historical Simulation, Value-at-Risk
JEL Classification: C14, C19
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
How Accurate are Value-at-Risk Models at Commercial Banks
By Jeremy Berkowitz and James M. O'brien
-
The Predictive Ability of Several Models of Exchange Rate Volatility
By Kenneth D. West and Dongchul Cho
-
Bank Capital and Value at Risk
By Patricia Jackson, David Maude, ...
-
Bank Capital Requirements for Market Risk: The Internal Models Approach
By Darryll Hendricks and Beverly Hirtle