Risk Management and Portfolio Optimization for Volatile Markets

  • Svetlozar T. Rachev
  • Borjana Racheva-Iotova
  • Stoyan V. Stoyanov
  • Frank J. Fabozzi


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.


Risk Measure Portfolio Optimization Asset Return Stable Distribution Sharpe Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Svetlozar T. Rachev
    • 1
  • Borjana Racheva-Iotova
    • 2
  • Stoyan V. Stoyanov
    • 2
  • Frank J. Fabozzi
    • 3
  1. 1.University of Karlsruhe, KIT, University of California at Santa Barbara and FinAnalytica Inc.Santa BarbaraUSA
  2. 2.FinAnalytica Inc.WashingtonUSA
  3. 3.Yale School of ManagementNew HavenUSA

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