Lasso-based simulation for high-dimensional multi-period portfolio optimization
Publication in refereed journal

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摘要This paper proposes a regression-based simulation algorithm for multi-period mean-variance portfolio optimization problems with constraints under a high-dimensional setting. For a high-dimensional portfolio, the least squares Monte Carlo algorithm for portfolio optimization can perform less satisfactorily with finite sample paths due to the estimation error from the ordinary least squares (OLS) in the regression steps. Our algorithm, which resolves this problem e, that demonstrates significant improvements in numerical performance for the case of finite sample path and high dimensionality. Specifically, we replace the OLS by the least absolute shrinkage and selection operator (lasso). Our major contribution is the proof of the asymptotic convergence of the novel lasso-based simulation in a recursive regression setting. Numerical experiments suggest that our algorithm achieves good stability in both low- and higher-dimensional cases.
著者Zhongyu Li, Ka Ho Tsang, Hoi Ying Wong
期刊名稱IMA Journal of Management Mathematics
出版年份2020
月份7
卷號31
期次3
出版社Oxford University Press
頁次257 - 280
國際標準期刊號1471-678X
語言美式英語

上次更新時間 2020-15-11 於 00:29