Analytic solution to variance optimization with no short positions. (12th December 2017)

• Record Type: Journal Article
• Authors: Kondor, Imre; Papp, Gábor; Caccioli, Fabio
• Is Part Of: Journal of statistical mechanics. (2017:Dec.)
• Publication Date: 2017-12-12
• Subjects: 7 -- 16 -- 12; Statistical mechanics -- Periodicals; Mechanics -- Statistical methods -- Periodicals; 530.1305
• DOI: 10.1088/1742-5468/aa9684
• Languages: English
• ISSNs: 1742-5468

Abstract

Abstract: We consider the variance portfolio optimization problem with a ban on short selling. We provide an analytical solution by means of the replica method for the case of a portfolio of independent, but not identically distributed, assets. We study the behavior of the solution as a function of the ratio r between the number N of assets and the length T of the time series of returns used to estimate risk. The no-short-selling constraint acts as an asymmetric ℓ 1 regularizer, setting some of the portfolio weights to zero and keeping the out-of-sample estimator for the variance bounded, avoiding the divergence present in the non-regularized case. However, the ban on short positions does not prevent the phase transition in the optimization problem, only shifts the critical point from its non-regularized value of r = 1 to 2, and changes its character: at r = 2 the out-of-sample estimator for the portfolio variance stays finite and the estimated in-sample variance vanishes, while another critical parameter, related to the estimated portfolio weights and the condensate density, diverges at the critical value r = 2 . We also calculate the distribution of the optimal weights over the random samples and show that the regularizer preferentially removes the assets with large variances, in accord with one's natural expectation.