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Activity Number:
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574
- Statistical Inference in Finance
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Marketing
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Abstract #329251
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Title:
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High-Dimensional Markowitz Portfolio Optimization Problem: Empirical Comparison of Covariance Matrix Estimators
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Author(s):
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Johan Lim* and Young-Geun Choi and Sujung Choi
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Companies:
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Seoul National University and Fred Hutchinson Cancer Research Center and Soongsil University
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Keywords:
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Markowitz's portfolio optimization;
minimum variance portfolio;
high-dimensional covariance matrix;
S&P500 data
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Abstract:
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We compare the performance of recently developed regularized covariance matrix estimators for Markowitz's portfolio optimization, minimum variance portfolio (MVP) problem in particular. We focus on seven estimators that are applied to the MVP problem in the literature, three of which regularize the eigenvalues of the sample covariance matrix while the other four assume sparsity of the true covariance matrix or its inverse. The comparisons are made with two sets of long-term S&P 500 stock return data that represent two extreme scenarios of active and passive managements. The results show that the MVPs with sparse covariance estimators have high Sharpe ratios, whereas the naive diversification (also known as "uniform (on market share) portfolio") still works well in terms of wealth growth.
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Authors who are presenting talks have a * after their name.
