Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82746
Title: Big data challenges of high-dimensional continuous-time mean-variance portfolio selection and a remedy
Authors: Chiu, Mei Choi
Pun, Chi Seng
Wong, Hoi Ying
Keywords: Constrained ℓ1 Minimization
Science::Mathematics
Constant‐rebalancing Portfolio
Issue Date: 2017
Source: Chiu, M. C., Pun, C. S., & Wong, H. Y. (2017). Big Data Challenges of High-Dimensional Continuous-Time Mean-Variance Portfolio Selection and a Remedy. Risk Analysis, 37(8), 1532-1549. doi:10.1111/risa.12801
Series/Report no.: Risk Analysis
Abstract: Investors interested in the global financial market must analyze financial securities internationally. Making an optimal global investment decision involves processing a huge amount of data for a high‐dimensional portfolio. This article investigates the big data challenges of two mean‐variance optimal portfolios: continuous‐time precommitment and constant‐rebalancing strategies. We show that both optimized portfolios implemented with the traditional sample estimates converge to the worst performing portfolio when the portfolio size becomes large. The crux of the problem is the estimation error accumulated from the huge dimension of stock data. We then propose a linear programming optimal (LPO) portfolio framework, which applies a constrained ℓ1 minimization to the theoretical optimal control to mitigate the risk associated with the dimensionality issue. The resulting portfolio becomes a sparse portfolio that selects stocks with a data‐driven procedure and hence offers a stable mean‐variance portfolio in practice. When the number of observations becomes large, the LPO portfolio converges to the oracle optimal portfolio, which is free of estimation error, even though the number of stocks grows faster than the number of observations. Our numerical and empirical studies demonstrate the superiority of the proposed approach.
URI: https://hdl.handle.net/10356/82746
http://hdl.handle.net/10220/49087
ISSN: 0272-4332
DOI: 10.1111/risa.12801
Rights: © 2017 Society for Risk Analysis . All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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