Asset Allocation Strategies Based on Penalized Quantile Regression
34 Pages Posted: 3 Jul 2015
Date Written: July 1, 2015
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: Suggested Citation