Diagnosis of systemic risk and contagion across financial sectors

TL;DR

This paper investigates simple, data-driven measures like cross-correlation and principal component analysis to diagnose systemic risk and contagion in financial markets, aiming to inform policymakers and investors about potential crises.

Contribution

It applies and discusses the effectiveness of accessible, non-parametric systemic risk measures to real financial data, enhancing early detection of systemic threats.

Findings

Cross-correlation increases before crises

Principal components capture systemic shifts

Measures provide early warning signals

Abstract

In normal times, it is assumed that financial institutions operating in non-overlapping sectors have complementary and distinct outcomes, typically reflected in mostly uncorrelated outcomes and asset returns. Such is the reasoning behind common "free lunches" to be had in investing, like diversifying assets across equity and bond sectors. Unfortunately, the recurrence of crises like the Great Financial Crisis of 2007-2008 demonstrate that such convenient assumptions often break down, with dramatic consequences for all financial actors. In hindsight, the emergence of systemic risk (as exemplified by failure in one part of a system spreading to ostensibly unrelated parts of the system) has been explained by narratives such as deregulation and leverage. But can we diagnose and quantify the ongoing emergence of systemic risk in financial systems? In this study, we focus on two previously-documented measures of systemic risk that require only easily available time series data (eg monthly asset returns): cross-correlation and principal component analysis. We apply these tests to daily and monthly returns on hedge fund indexes and broad-based market indexes, and discuss their results. We hope that a frank discussion of these simple, non-parametric measures can help inform legislators, lawmakers, and financial actors of potential crises looming on the horizon.