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Building a Better Fund of Hedge Funds: A Fractal and Alpha - Stable Distribution Approach


  • Yan Olszewski

    (Maple Financial Alternative Investments)


Markowitz’s (1952) portfolio theory has permeated financial institutions over the past 50 years. Assuming that returns are normally distributed, Markowitz suggests that portfolio optimization should be performed in a mean-variance framework. With the emergence of hedge funds and their non-normally distributed returns, mean-variance portfolio optimization is no longer adequate. Here, hedge fund returns are modeled with the alpha-stable distribution and a mean-CVaR portfolio optimization is performed. Results indicate that by using the alpha- stable distribution, a more efficient fund of hedge funds portfolio can be created than would be by assuming a normal distribution. To further increase efficiency, the Hurst exponent is considered as a filtering tool and it is found that combining hedge fund strategies within a range of Hurst exponents leads to the creation of more efficient portfolios as characterized by higher risk-adjusted ratios. These findings open the door for the further study of econophysics tools in the analysis of hedge fund returns.

Suggested Citation

  • Yan Olszewski, 2005. "Building a Better Fund of Hedge Funds: A Fractal and Alpha - Stable Distribution Approach," Finance 0507018, University Library of Munich, Germany, revised 13 Dec 2005.
  • Handle: RePEc:wpa:wuwpfi:0507018
    Note: Type of Document - pdf; pages: 37

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    References listed on IDEAS

    1. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
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    More about this item


    hedge funds; fund of funds; portfolio optimization; conditional value at risk; alpha-stable distribution; Hurst exponent; fractals;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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