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    Item type:Publication,
    Essays on characteristic-based portfolio optimization
    In my dissertation, I develop new portfolio optimization methodologies designed to enhance empirical performance in practical applications. Instead of relying on asset characteristics as intermediary variables to estimate the distribution of asset returns—an approach that introduces estimation errors—this work explores methods that directly incorporate asset characteristics as input variables in the optimization program. By bypassing the estimation step, these approaches aim to improve robustness and efficiency in portfolio construction. This dissertation contributes to both active and passive portfolio management.
    doctoral thesis
      82  169
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    Item type:Publication,
    Enhancing index-tracking performance: Leveraging characteristic-based factor models for reduced estimation errors
    This paper addresses the challenge of minimizing tracking error in passive portfolio management by reducing estimation errors commonly encountered in traditional optimization methods. We introduce an innovative cardinality-constrained mixed-integer optimization framework that incorporates characteristic-based factor models to enhance index-tracking performance. By leveraging these models, our approach aims to minimize errors stemming from estimation uncertainty. In an empirical analysis, we benchmark the tracking errors of our approach against traditional methods, examining both linear and quadratic programs. We further evaluate robustness across various stock market indices, time periods, solvers, and transaction costs. The results indicate that our method consistently reduces estimation errors, achieving superior tracking performance relative to conventional techniques. These findings provide crucial guidance for efficiently optimizing index-tracking portfolios while accommodating practical constraints.
    journal article
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