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  • Risk Assessment and Asset Allocation with Gross Exposure Constraints for Vast Portfolios

    Fri, Sep 04, 2009 @ 03:30 PM - 05:00 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

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    Mathematical Finance ColloquiumWhen: Friday, September 4, 2009, 3:30 PMWhere: KAP 414Title: "Risk Assessment and Asset Allocation with Gross Exposure Constraints for Vast Portfolios"Speaker: Jianqing Fan Frederick L. Moore Professor of Finance, and the Director of Committee of Statistical Studies in the Department of Operation Research and Financial Engineering of Princeton UniversityABSTRACT: Markowitz (1952, 1959) laid down the ground-breaking work on the
    mean-variance analysis. Under his framework, the theoretical optimal allocation
    vector can be very different from the estimated one for large portfolios due to the
    intrinsic difficulty of estimating a vast covariance matrix and return vector. This
    can result in adverse performance in portfolio selected based on empirical data
    due to the accumulation of estimation errors. We address this problem by
    introducing the gross-exposure constrained mean-variance portfolio selection.
    We show that with gross-exposure constraint the empirically selected optimal
    portfolios based on estimated covariance matrices have similar performance
    to the theoretical optimal portfolios and there is no error accumulation effect
    from estimation of vast covariance matrices. This gives theoretical justification
    to the empirical results in Jagannathan and Ma (2003). We also show that the
    no-short-sale portfolio is not diversified enough and can be improved by
    allowing some short positions. As the constraint on short sales relaxes, the
    number of selected assets gradually increases and finally reaches the total
    number of stocks when tracking portfolios or selecting assets. This achieves
    the optimal sparse portfolio selection, which has close performance to the
    theoretical optimal one. Among 1000 stocks, for example, we are able to identify
    all optimal subsets of portfolios of different sizes, their associated allocation
    vectors, and their estimated risks. The utility of our new approach is illustrated
    by simulation and empirical studies on the 100 Fama-French industrial portfolios
    and the 600 stocks randomly selected from Russell 3000.

    Location: Kaprielian Hall (KAP) - 414

    Audiences: Everyone Is Invited

    Contact: Georgia Lum

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