Stochastic Models for Risk Estimation in Volatile Markets: A Survey

22 Pages Posted: 24 Dec 2010

See all articles by Stoyan V. Stoyanov

Stoyan V. Stoyanov

Charles Schwab

Svetlozar Rachev

Texas Tech University

Boryana Racheva-Iotova

affiliation not provided to SSRN

Frank J. Fabozzi

EDHEC Business School

Date Written: October 24, 2008

Abstract

Portfolio risk estimation in volatile markets requires employing fat-tailed models for financial returns combined with copula functions to capture asymmetries in dependence and an appropriate downside risk measure. In this survey, we discuss how these three essential components can be combined together in a Monte Carlo based framework for risk estimation and risk capital allocation with the average value-at-risk measure (AVaR). AVaR is the average loss provided that the loss is larger than a predefined Value-at-Risk level. We consider in some detail the AVaR calculation and estimation and investigate the stochastic stability.

Keywords: Fat-Tailed Distributions, Stable Distributions, Downside Risk, Average Value-at-Risk, Conditional Value-at-Risk, Risk Budgeting

JEL Classification: G32, C16, C61

Suggested Citation

Stoyanov, Stoyan Veselinov and Rachev, Svetlozar and Racheva-Iotova, Boryana and Fabozzi, Frank J., Stochastic Models for Risk Estimation in Volatile Markets: A Survey (October 24, 2008). Annals of Operation Research, Vol. 176, No. 1, 2010, Available at SSRN: https://ssrn.com/abstract=1730212

Stoyan Veselinov Stoyanov (Contact Author)

Charles Schwab ( email )

101 Montgomery Street (120K-15)
San Francisco, CA 94104
United States

Svetlozar Rachev

Texas Tech University ( email )

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

Boryana Racheva-Iotova

affiliation not provided to SSRN ( email )

Frank J. Fabozzi

EDHEC Business School ( email )

France
215 598-8924 (Phone)

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