Risk, VaR, CVaR And Their Associated Portfolio Optimizations When Asset Returns Have A Multivariate Student T Distribution

The authors show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of systems to be carefully attributed between choices of risk function (e.g. VaR vs. CVaR); choice of return distribution (power law tail vs. Gaussian) and choice of event frequency, for risk assessment. They exploit this to provide a simple method for portfolio optimization when the asset returns follow a standard multivariate T distribution. This may be used as a semi-analytical verification tool for more general optimizers, and for practical assessment of the impact of fat tails on asset allocation for shorter time horizons.

Provided by: Cornell University Topic: Data Management Date Added: Feb 2011 Format: PDF

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