Risk Estimation for GARCH Processes with Heavy-Tailed Innovations

56 Pages Posted: 18 Jan 2019

See all articles by Silke Prohl

Silke Prohl

Princeton University; NYU

Georgi K. Mitov

Bulgarian Academy of Science

Svetlozar Rachev

Texas Tech University

Frank J. Fabozzi

EDHEC Business School

Aaron Kim

affiliation not provided to SSRN

Date Written: September 27, 2011


The standard measures of risk such as RiskMetrics compute the Value-at-Risk as the maximum probability of loss of an investment over a certain period of time given a chosen confidence level. There are non-parametric and parametric methods to compute VaR. A non-parametric approach simulates the probability of distribution of future returns. The unconditional parametric approach assumes that location and scale components of the returns are constant and reduces the VaR problem to computing the γ-quantile of the returns for a given nominal level γ. A conditional approach assumes non-constant location and scale components. A standard implementation of this approach involves the ARCH/GARCH models. However, the popularity of this approach for risk management is restricted due to risk of misspecification of a GARCH model and of distributions for its conditional innovations. In this paper we focus on investigation of the parametric conditional approach under these both problems.

The empirical limitation of the assumption of a Gaussian distributions of portfolio returns has been well documented in the empirical research conducted over the last more than twenty years. It has been shown in several studies that returns exhibit high kurtosis and skewness that are incompatible with the normality assumptions (see, Fama (1965), Blattberg and Gonedes (1974), Marinelli et.al (2006)). A number of studies have showed how dramatically differ the estimates of VaR obtained under wrong assumptions with respect to the underlying return process (see, Beder, 1995).

Some recently proposed risk measure frameworks deal with these futures of high-frequency financial data. One natural approach to overcome these inconsistencies is to adopt the model with heavy-tailed innovations (Frey and McNeil 2000, Marinelli et al. 2004, Hang Chan et al. (2007)....).
A GARCH model with generalized Pareto distribution for the innovations was considered in Frey and McNeil (2000). They proposed a two-step procedure to compute conditional VaR for GARCH model. Hang Chan et al. (2007) work under different assumption than of Pareto distributed innovations, they suppose the model with heavy-tailed innovations. They compute VaR within the non-parametric framework. However, this paper does not provide a backtesting results. Marinelli et al. (2004) assume that returns are heavy-tailed, e.g., follow a stable law and provide comparison of the approach based on assumption of Paretian stable returns with the approach based on assumption of Gaussian returns and on the Extreme Value Theory. However, this paper does not consider the parametric method for GARCH model with heavy-tailed innovations.

Our work is closely related to the last two papers. Our contribution consist of numerical study which compares a backtesting procedure of alternative scheme to compute VaR. We consider the estimates for VaR and Expected Shortfalls (CVaR).

Suggested Citation

Prohl, Silke and Mitov, Georgi K. and Rachev, Svetlozar and Fabozzi, Frank J. and Kim, Aaron, Risk Estimation for GARCH Processes with Heavy-Tailed Innovations (September 27, 2011). Available at SSRN: https://ssrn.com/abstract=3312569 or http://dx.doi.org/10.2139/ssrn.3312569

Silke Prohl (Contact Author)

Princeton University ( email )

Sherrerd Hall, Charlton Street
Princeton, NJ 08544
United States

NYU ( email )

251 Mercer Street
New York, NY - 10012
United States

Georgi K. Mitov

Bulgarian Academy of Science ( email )

1, 15 Noemvri Str.
1040 Sofia, 1113
United States

Svetlozar Rachev

Texas Tech University ( email )

Dept of Mathematics and Statistics
Lubbock, TX 79409
United States
631-662-6516 (Phone)

Frank J. Fabozzi

EDHEC Business School ( email )

215 598-8924 (Phone)

Aaron Kim

affiliation not provided to SSRN

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics