Portfolio Selection: An Extreme Value Approach
Author(s) -
Francis J. DiTraglia,
Jeffrey R. Gerlach
Publication year - 2011
Publication title -
ssrn electronic journal
Language(s) - English
Resource type - Journals
ISSN - 1556-5068
DOI - 10.2139/ssrn.1929425
Subject(s) - portfolio , selection (genetic algorithm) , extreme value theory , value (mathematics) , actuarial science , computer science , economics , financial economics , mathematics , statistics , artificial intelligence
We show theoretically that lower tail dependence (χ), a measure of the probability that a portfolio will suffer large losses given that the market does, contains important information for risk-averse investors. We then estimate χ for a sample of DJIA stocks and show that it differs systematically from other risk measures including variance, semi-variance, skewness, kurtosis, beta, and coskewness. In out-of-sample tests, portfolios constructed to have low values of χ outperform the market index, the mean return of the stocks in our sample, and portfolios with high values of χ. Our results indicate that χ is conceptually important for risk-averse investors, differs substantially from other risk measures, and provides useful information for portfolio selection.
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