Mean-Variance-VaR portfolios: MIQP formulation and performance analysis

TL;DR

This paper introduces a new portfolio optimization model combining mean, variance, and VaR, formulated as an MIQP problem, and demonstrates its practical effectiveness through empirical analysis on real datasets.

Contribution

It proposes a novel MIQP formulation for Mean-Variance-VaR portfolio optimization, addressing computational challenges and improving out-of-sample performance.

Findings

The MIQP model effectively incorporates VaR into portfolio optimization.

Empirical results show improved out-of-sample performance over traditional models.

The approach is practically applicable to real-world financial datasets.

Abstract

Value-at-Risk is one of the most popular risk management tools in the financial industry. Over the past 20 years several attempts to include VaR in the portfolio selection process have been proposed. However, using VaR as a risk measure in portfolio optimization models leads to problems that are computationally hard to solve. In view of this, few practical applications of VaR in portfolio selection have appeared in the literature up to now. In this paper, we propose to add the VaR criterion to the classical Mean-Variance approach in order to better address the typical regulatory constraints of the financial industry. We thus obtain a portfolio selection model characterized by three criteria: expected return, variance, and VaR at a specified confidence level. The resulting optimization problem consists in minimizing variance with parametric constraints on the levels of expected return and VaR. This model can be formulated as a Mixed-Integer Quadratic Programming (MIQP) problem. An extensive empirical analysis on seven real-world datasets demonstrates the practical applicability of the proposed approach. Furthermore, the out-of-sample performance of the optimal Mean-Variance-VaR portfolios seems to be generally better than that of the optimal Mean-Variance and Mean-VaR portfolios.