Measuring the frequency dynamics of financial connectedness and systemic risk

TL;DR

This paper introduces a spectral-based framework to measure financial connectedness across different time horizons, revealing how systemic risk propagates differently in short, medium, and long-term cycles.

Contribution

It develops a novel spectral approach to analyze frequency-dependent financial connectedness, capturing dynamic systemic risk across various time scales.

Findings

High-frequency connectedness indicates rapid information processing and short-term shock impacts.

Low-frequency connectedness suggests persistent shocks with longer transmission periods.

Empirical analysis shows diverse time-frequency dynamics in US financial institutions.

Abstract

We propose a new framework for measuring connectedness among financial variables that arises due to heterogeneous frequency responses to shocks. To estimate connectedness in short-, medium-, and long-term financial cycles, we introduce a framework based on the spectral representation of variance decompositions. In an empirical application, we document the rich time-frequency dynamics of volatility connectedness in US financial institutions. Economically, periods in which connectedness is created at high frequencies are periods when stock markets seem to process information rapidly and calmly, and a shock to one asset in the system will have an impact mainly in the short term. When the connectedness is created at lower frequencies, it suggests that shocks are persistent and are being transmitted for longer periods.