Mitigating Extremal Risks: A Network-Based Portfolio Strategy

TL;DR

This paper introduces a novel network-based portfolio strategy that leverages extreme value theory and graph partitioning to mitigate extremal risks in volatile financial markets.

Contribution

It develops a new approach combining extremal dependence networks and graph theory to enhance risk diversification in portfolio management.

Findings

Proposes a network model reflecting extremal dependencies between stocks.

Utilizes maximum independent set and partitioning to optimize portfolio diversification.

Demonstrates improved risk measures compared to market portfolios.

Abstract

In financial markets marked by inherent volatility, extreme events can result in substantial investor losses. This paper proposes a portfolio strategy designed to mitigate extremal risks. By applying extreme value theory, we evaluate the extremal dependence between stocks and develop a network model reflecting these dependencies. We use a threshold-based approach to construct this complex network and analyze its structural properties. To improve risk diversification, we utilize the concept of the maximum independent set from graph theory to develop suitable portfolio strategies. Since finding the maximum independent set in a given graph is NP-hard, we further partition the network using either sector-based or community-based approaches. Additionally, we use value at risk and expected shortfall as specific risk measures and compare the performance of the proposed portfolios with that of the market portfolio.