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Asset Allocation Strategies Based on Penalized Quantile Regression

Giovanni Bonaccolto, Massimiliano Caporin () and Sandra Paterlini

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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.

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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