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Asset Allocation Strategies Based On Penalized Quantile Regression

Giovanni Bonaccolto (), Massimiliano Caporin () and Sandra Paterlini ()
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Giovanni Bonaccolto: University of Padova
Sandra Paterlini: European Business School

No 199, "Marco Fanno" Working Papers from Dipartimento di Scienze Economiche "Marco Fanno"

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. (search for similar items in EconPapers)
JEL-codes: C58 G10 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-rmg
Date: 2015-07
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Journal Article: Asset allocation strategies based on penalized quantile regression (2018) Downloads
Working Paper: Asset Allocation Strategies Based on Penalized Quantile Regression (2015) Downloads
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