Risk Management and Portfolio Optimization for Volatile Markets

We describe a framework for risk estimation and portfolio optimization based on stable distributions and the average value-at-risk risk measure. In contrast to normal distributions, stable distributions capture the fat tails and the asymmetric nature of real-world risk factor distributions. In addition, we make use of copulas, a generalization of overly restrictive linear correlation models, to account for the dependencies between risk factors during extreme events. Using superior models, VaR becomes a much more accurate measure of downside risk. More importantly, stable expected tail loss (SETL) can be accurately calculated and used as a more informative risk measure. Along with being a superior risk measure, SETL enables an elegant approach to risk budgeting and portfolio optimization. Finally, we mention the alternative investment performance measurement tools.

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