AN EXTREME VALUE THEORY APPROACH TO THE ALLOCATION OF MULTIPLE ASSETS

2004 ◽  
Vol 07 (08) ◽  
pp. 1031-1068 ◽  
Author(s):  
BRENDAN O. BRADLEY ◽  
MURAD S. TAQQU

We investigate the portfolio construction problem for risk-averse investors seeking to minimize quantile based measures of risk. Using dependence measures from extreme value theory, we find that most international equity markets are asymptotically independent. We also find that the few cases of asymptotic dependence occur mostly in markets which are in close geographic proximity. We then examine how extremal dependence affects the asset allocation problem. Following the structure variable approach, we focus on the portfolio and model its tail in a manner consistent with extreme value theory. We then develop a methodology for asset allocation where the goal is to guard against catastrophic losses. The methodology is tested through simulations and applied to portfolios made up of two or more international equity markets. We analyze in detail three typical types of markets, one where the assets are asymptotically independent and the ratio of marginal risks is not constant, the second where the assets are asymptotically independent but the ratio of marginal risks are approximately constant and the third where the assets are asymptotically dependent and the ratio of marginal risks is not constant. The results are compared with the optimal portfolio under the assumption of normally distributed returns. Surprisingly, we find that the assumption of normality incurs only a modest amount of extra risk for all but the largest losses. We make the software written in support of this work freely available and describe its use in the appendix.

2019 ◽  
Vol 98 ◽  
pp. 1-22 ◽  
Author(s):  
Mohamed Arouri ◽  
Oussama M’saddek ◽  
Duc Khuong Nguyen ◽  
Kuntara Pukthuanthong

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1425
Author(s):  
Miloš Božović

This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasonable level of significance, even when periods of stress are included. Numerical simulations corroborate the empirical results.


2019 ◽  
Vol 99 (2) ◽  
Author(s):  
Nicolas Ponthus ◽  
Julien Scheibert ◽  
Kjetil Thøgersen ◽  
Anders Malthe-Sørenssen ◽  
Joël Perret-Liaudet

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