J. Kim, W. Kim, Yongjae Lee, Bong-Geun Choi, Frank J. Fabozzi
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Portfolio optimization is the basic quantitative approach for finding optimal portfolio weights. It has become increasingly important as portfolio construction involves more and more data and automated approaches. The inherent uncertainty in financial markets has led to consistent demand for improved robustness of portfolio models. In this article, the authors discuss the importance of robustness in portfolio optimization and present powerful methods that include robust estimators, robust portfolio optimization, distributionally robust optimization, and scenario-based optimization. They also review data-driven methods, machine learning–based models, and practical approaches for improving portfolio robustness.
期刊介绍:
Founded by Peter Bernstein in 1974, The Journal of Portfolio Management (JPM) is the definitive source of thought-provoking analysis and practical techniques in institutional investing. It offers cutting-edge research on asset allocation, performance measurement, market trends, risk management, portfolio optimization, and more. Each quarterly issue of JPM features articles by the most renowned researchers and practitioners—including Nobel laureates—whose works define modern portfolio theory.