Portfolio optimization with idiosyncratic and systemic risks for financial networks

TL;DR

This paper introduces a multi-objective portfolio optimization model that incorporates both idiosyncratic and systemic risks using financial network analysis, leading to portfolios with better returns and lower drawdowns.

Contribution

It develops a novel multi-objective optimization framework that integrates network-derived systemic risk measures into asset allocation.

Findings

Portfolios optimized with network-based systemic risk measures outperform traditional methods.

The approach results in portfolios with higher returns in out-of-sample tests.

Optimized portfolios exhibit less drawdown during market downturns.

Abstract

In this study, we propose a new multi-objective portfolio optimization with idiosyncratic and systemic risks for financial networks. The two risks are measured by the idiosyncratic variance and the network clustering coefficient derived from the asset correlation networks, respectively. We construct three types of financial networks in which nodes indicate assets and edges are based on three correlation measures. Starting from the multi-objective model, we formulate and solve the asset allocation problem. We find that the optimal portfolios obtained through the multi-objective with networked approach have a significant over-performance in terms of return measures in an out-of-sample framework. This is further supported by the less drawdown during the periods of the stock market fluctuating downward. According to analyzing different datasets, we also show that improvements made to portfolio strategies are robust.