Scale-consistent Value-at-Risk [An article from: Finance Research Letters]

This digital document is a journal article from Finance Research Letters, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Returns in financial assets show consistent excess kurtosis and skewness, indicating the presence of large fluctuations not predicted by Gaussian models. In this paper we propose a generalization to the popular RiskMetrics approach to Value-at-Risk. In order to model scale-consistent Value-at-Risk (VaR), we propose a model with a time varying scale parameter and error terms that are truncated Levy distributed. Levy flights include a method for scaling up from a single-day volatility to a multi-day volatility. We use this rule to approximate future volatility and estimate Value-at-Risk several days ahead, and compare it to the popular approach, which is a special case of our method. Back-testing results suggest that the inclusion of more sophisticated tail properties and the data-driven scaling rule improves the performance of the VaR model significantly, for short and long time horizons. Our approach is easier to implement and is less time and computer intensive compared to Monte Carlo simulation methods.