Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When?

TL;DR

This paper introduces a new method to analyze global bank network connectedness across time and frequency domains, distinguishing common and idiosyncratic shocks, with empirical validation on stock volatility data.

Contribution

It develops a novel high-dimensional decomposition approach using factor models and spectral analysis to separate common and idiosyncratic influences on bank connectedness.

Findings

SWC spikes during crises driven by common shocks

Normal times dominated by idiosyncratic and medium-term shocks

Method provides bootstrap confidence bands for SWC measures

Abstract

We propose a novel approach to estimate high-dimensional global bank network connectedness in both the time and frequency domains. By employing a factor model with sparse VAR idiosyncratic components, we decompose system-wide connectedness (SWC) into two key drivers: (i) common component shocks and (ii) idiosyncratic shocks. We also provide bootstrap confidence bands for all SWC measures. Furthermore, spectral density estimation allows us to disentangle SWC into short-, medium-, and long-term frequency responses to these shocks. We apply our methodology to two datasets of daily stock price volatilities for over 90 global banks, spanning the periods 2003-2013 and 2014-2023. Our empirical analysis reveals that SWC spikes during global crises, primarily driven by common component shocks and their short term effects. Conversely, in normal times, SWC is largely influenced by idiosyncratic shocks and medium-term dynamics.