On the Conditional Value at Risk Based on the Laplace Distribution with Application in GARCH Model

Abstract

In this article, the Laplace distribution is employed in lieu of the well-known normal distribution for finding better scalar values of risk. Explicit formulas for value-at-risk (VaR) and conditional value-at-risk (CVaR) are studied and used to manage the risk involved in a stock movement by using the GARCH model. Numerical simulations are given for a variety of stocks in equity markets to uphold the findings.

Keywords

• risk measure
• stock market
• Laplace distribution
• non-normality
• fat-tail