# Asset allocation strategies based on penalized quantile regression

Giovanni Bonaccolto (), Massimiliano Caporin () and Sandra Paterlini ()
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Giovanni Bonaccolto: University “Kore” of Enna
Sandra Paterlini: European Business School

Computational Management Science, 2018, vol. 15, issue 1, 1-32

Abstract: Abstract It is well known that the quantile regression model used as an asset allocation tool minimizes the portfolio extreme risk whenever the attention is placed on the lower quantiles of the response variable. By considering the entire conditional distribution of the dependent variable, we show that it is possible to obtain further benefits by optimizing different risk and performance indicators. In particular, we introduce a risk-adjusted profitability measure, useful in evaluating financial portfolios from a ‘cautiously optimistic’ perspective, as 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 $$\ell _1$$ ℓ 1 -norm penalty on the assets’ weights.

Keywords: Quantile regression; $$\ell _1$$ ℓ 1 -Norm penalty; Asset allocation (search for similar items in EconPapers)
Date: 2018
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Related works:
Working Paper: Asset Allocation Strategies Based on Penalized Quantile Regression (2015)
Working Paper: Asset Allocation Strategies Based On Penalized Quantile Regression (2015)
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