Exponentially Smoothing the Skewed Laplace Distribution for Value-at-Risk Forecasting
Article first published online: 25 JUN 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 32, Issue 6, pages 534–550, September 2013
How to Cite
Gerlach, R., Lu, Z. and Huang, H. (2013), Exponentially Smoothing the Skewed Laplace Distribution for Value-at-Risk Forecasting. J. Forecast., 32: 534–550. doi: 10.1002/for.2255
- Issue published online: 26 JUL 2013
- Article first published online: 25 JUN 2013
- asymmetric Laplace distribution;
- exponential smoothing;
- skewness and heavy tails;
- time-varying parameters;
- value-at-risk (VaR)
Value-at-risk (VaR) is a standard measure of market risk in financial markets. This paper proposes a novel, adaptive and efficient method to forecast both volatility and VaR. Extending existing exponential smoothing as well as GARCH formulations, the method is motivated from an asymmetric Laplace distribution, where skewness and heavy tails in return distributions, and their potentially time-varying nature, are taken into account. The proposed volatility equation also involves novel time-varying dynamics. Back-testing results illustrate that the proposed method offers a viable, and more accurate, though conservative, improvement in forecasting VaR compared to a range of popular alternatives. Copyright © 2013 John Wiley & Sons, Ltd.