Ranking the Predictive Performances of Value-at-Risk Estimation Methods
25 Pages Posted: 2 Oct 2010
Date Written: March 17, 2010
Abstract
We propose a novel ranking model and a complementary predictive ability test statistic to investigate the forecasting performances of different Value at Risk (VaR) methods. The ranking model develops a unified framework which penalizes excessive capital allocation, autocorrelation of violations and violation magnitudes through an anisometric structure. In addition, as an alternative to existing predictive ability tests which compare the forecasting methods two at a time, our complementary test statistic considers all methods at the same time to confirm whether the chosen method outperforms the competing methods. The results show that asymmetric methods such as CAViaR Asymmetric and EGARCH generate the best performing forecasts. This suggests that the performance of VaR methods does not depend entirely on whether they are parametric, non-parametric, semi-parametric or hybrid; but rather if they can effectively model the asymmetry of the underlying data or not.
Keywords: Value-at-Risk, loss function, predictive ability test
JEL Classification: C14, C15, C23, C59, G21, G28, G32
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Tests of Equal Forecast Accuracy and Encompassing for Nested Models
-
Long Swings in the Exchange Rate: are They in the Data and Do Markets Know it?
-
Exchange Rates and Fundamentals
By Charles M. Engel and Kenneth D. West
-
Exchange Rates and Fundamentals
By Charles M. Engel and Kenneth D. West
-
Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?
-
Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?
By Lutz Kilian
-
Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?
By Yin-wong Cheung, Menzie David Chinn, ...