# Critical Decisions for Asset Allocation via Penalized Quantile Regression

@article{Bonaccolto2019CriticalDF,
title={Critical Decisions for Asset Allocation via Penalized Quantile Regression},
author={Giovanni Bonaccolto},
journal={Mutual Funds},
year={2019}
}
• G. Bonaccolto
• Published 13 August 2019
• Computer Science, Economics
• Mutual Funds
We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state\textendash of\textendash art asset allocation rules. First, we use a post\textendash penalization procedure to deal with overshrinking and concentration issues. Second, we investigate whether and to what extent the performance changes when moving from convex to nonconvex penalty functions. Third, we compare different methods to select the optimal…

## References

SHOWING 1-10 OF 40 REFERENCES
Constructing optimal sparse portfolios using regularization methods
• Computer Science
Comput. Manag. Sci.
• 2015
This work proposes a new, simple type of penalty that explicitly considers financial information and then considers several alternative penalties, that allow to improve on the $$\ell _{1}$$ℓ1-regularization approach, and shows empirically that the proposed penalties can lead to the construction of portfolios with an out-of-sample performance superior to several state- of-art benchmarks, especially in high dimensional problems.
Dynamic semiparametric models for expected shortfall (and Value-at-Risk)
• Economics, Computer Science
Journal of Econometrics
• 2019
This work uses recent results from statistical decision theory to overcome the problem of "elicitability" for ES by jointly modelling ES and VaR, and proposes new dynamic models for these risk measures.
Sparse and stable Markowitz portfolios
• Economics, Mathematics
Proceedings of the National Academy of Sciences
• 2009
This work proposes to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights, which regularizes (stabilizes) the optimization problem, encourages sparse portfolios, and allows accounting for transaction costs.
L1-Norm Quantile Regression
• Mathematics
• 2008
Classical regression methods have focused mainly on estimating conditional mean functions. In recent years, however, quantile regression has emerged as a comprehensive approach to the statistical
A Global Optimization Heuristic for Portfolio Choice with VaR and Expected Shortfall
• Economics
• 2002
Constraints on downside risk, measured by shortfall probability, expected shortfall etc., lead to optimal asset allocations which differ from the mean-variance optimum. The resulting optimization
Optimal Expected-Shortfall Portfolio Selection with Copula-Induced Dependence
• Mathematics
• 2018
ABSTRACT We provide a computational framework for the selection of weights that minimize the expected shortfall of the aggregated risk . Contrary to classic and recent results, we neither restrict
A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms
• Mathematics, Computer Science
Manag. Sci.
• 2009
A general nonlinear programming framework for identifying portfolios that have superior out-of-sample performance in the presence of estimation error and it is found that the norm-constrained portfolios the authors propose outperform shortsale- Constrained portfolio approaches, shrinkage approaches, the 1/N portfolio, factor portfolios, and also other strategies considered in the literature.
Optimization of conditional value-at risk
• Economics
• 2000
A new approach to optimizing or hedging a portfolio of nancial instruments to reduce risk is presented and tested on applications. It focuses on minimizing Conditional Value-at-Risk (CVaR) rather
Risk minimization in multi-factor portfolios: What is the best strategy?
• Economics, Computer Science
Ann. Oper. Res.
• 2018
An extensive analysis of eight state-of-the-art risk-minimization schemes and compares risk factor performance in a conditional performance analysis, contrasting good and bad states of the economy shows that each single factor yields positive premia in exchange for risk.
Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?
• Economics
• 2009
We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio. Of the 14 models we