Lasso-based simulation for high-dimensional multi-period portfolio optimization
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AbstractThis 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.
All Author(s) ListZhongyu Li, Ka Ho Tsang, Hoi Ying Wong
Journal nameIMA Journal of Management Mathematics
Volume Number31
Issue Number3
PublisherOxford University Press
Pages257 - 280
LanguagesEnglish-United States

Last updated on 2020-23-10 at 01:05