Regularized Mimicking Portfolios

41 Pages Posted: 17 Mar 2022

See all articles by Dan Luo

Dan Luo

Temple University - Department of Finance

Oleg Rytchkov

Temple University - Department of Finance

Xun Zhong

Fordham University - Finance Area

Date Written: February 18, 2022

Abstract

We propose new approaches to constructing mimicking portfolios for non-tradable shocks from a large set of base assets. The key element of our procedure is the imposition of regularization constraints on portfolio strategies that help mitigate the overfitting problem caused by a large number of statistical moments that determine optimal portfolio weights. We empirically explore the out-of-sample performance of mimicking portfolios for nine macroeconomic and uncertainty shocks obtained by applying the proposed techniques. In all cases, our mimicking portfolios have less extreme weights than those produced by standard methods without sacrificing the portfolio performance. When shocks can be mimicked by stock returns, the performance of our portfolios is superior to the performance of their unregularized counterparts.

Keywords: mimicking portfolio, regularization, cross-validation, GMM

JEL Classification: G11, C55

Suggested Citation

Luo, Dan and Rytchkov, Oleg and Zhong, Xun, Regularized Mimicking Portfolios (February 18, 2022). Available at SSRN: https://ssrn.com/abstract=4038417 or http://dx.doi.org/10.2139/ssrn.4038417

Dan Luo

Temple University - Department of Finance ( email )

Fox School of Business and Management
Philadelphia, PA 19122
United States

Oleg Rytchkov (Contact Author)

Temple University - Department of Finance ( email )

Fox School of Business and Management
Philadelphia, PA 19122
United States

Xun Zhong

Fordham University - Finance Area ( email )

45 Columbus Avenue, Room 620
New York, NY 10023
United States

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