Asset Allocation Strategies Based on Penalized Quantile Regression

34 Pages Posted: 3 Jul 2015

See all articles by Giovanni Bonaccolto

Giovanni Bonaccolto

University "Kore" of Enna

Massimiliano Caporin

University of Padua - Department of Statistical Sciences

Sandra Paterlini

University of Trento - Department of Economics and Management

Date Written: July 1, 2015

Abstract

It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and performance indicators. In particular, we introduce a risk-adjusted profitability measure, useful in evaluating financial portfolios under a pessimistic perspective, since the reward contribution is net of the most favorable outcomes. Moreover, as we consider large portfolios, we also cope with the dimensionality issue by introducing an l1-norm penalty on the assets weights.

Keywords: Quantile regression, l1-norm penalty, pessimistic asset allocation

JEL Classification: C58, G10

Suggested Citation

Bonaccolto, Giovanni and Caporin, Massimiliano and Paterlini, Sandra, Asset Allocation Strategies Based on Penalized Quantile Regression (July 1, 2015). Available at SSRN: https://ssrn.com/abstract=2625584 or http://dx.doi.org/10.2139/ssrn.2625584

Giovanni Bonaccolto (Contact Author)

University "Kore" of Enna ( email )

Viale delle Olimpiadi
Enna, 94100
Italy

Massimiliano Caporin

University of Padua - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

Sandra Paterlini

University of Trento - Department of Economics and Management ( email )

Via Inama 5
Trento, I-38100
Italy

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