Portfolio Allocation under Asymmetric Dependence in Asset Returns using Local Gaussian Correlations

TL;DR

This paper introduces a nonparametric local Gaussian correlation measure to capture asymmetric dependence in asset returns, enhancing portfolio allocation by extending the mean-variance framework and outperforming traditional methods.

Contribution

It develops a novel nonparametric dependence measure, the local Gaussian correlation, and integrates it into portfolio optimization, improving performance over classical approaches.

Findings

Outperforms equally weighted portfolios

Outperforms classical Markowitz portfolios

Effective in capturing asymmetric dependence

Abstract

It is well known that there are asymmetric dependence structures between financial returns. In this paper we use a new nonparametric measure of local dependence, the local Gaussian correlation, to improve portfolio allocation. We extend the classical mean-variance framework, and show that the portfolio optimization is straightforward using our new approach, only relying on a tuning parameter (the bandwidth). The new method is shown to outperform the equally weighted (1/N) portfolio and the classical Markowitz portfolio for monthly asset returns data.