On the Risk-based Contagion of G7 Banking System and the COVID-19 Pandemic

Abstract

We revisit the discussion on banking system contagion by proposing a risk-based empirical analysis during the current pandemic period. We use daily returns on G7 banking sector indices from 1 January 2015 to 31 December 2019 (pre-pandemic), and from 1 January 2020 to 16 October 2020 (pandemic). Based on the dissimilarities, the pandemic has intensified banking contagion. Frequency-based Granger causality is useful to tell the history of the pass-through of this health crisis across G7 banking sectors. We highlight the increase in the predictive relevance of Italian banking cycles during the pandemic. VaR ratio analysis, considering 21 possible pairwise combinations with the G7 financial indices, suggests a stronger contagion between banking systems. The greatest contagion is evident in the Italian and French banking systems, countries severely affected by deaths by COVID-19, while we find less contagion between Japan and Germany, countries least affected by the first wave of COVID-19.

Explaining the first effects of Covid-19 on Greek banks’ profitability

“…In recent years, the banking system has been in a process of constant adaptation and renewal, with the aim of reducing costs, keeping up with customer expectations and accelerating digitalisation, due to the increased need for innovation and digital strategies, an element to which the emergence of Covid-19 has contributed. Studying the daily returns of G7 banking indices, Matos et al (2021) find that, since the outbreak of the pandemic, the contagion of crises in the financial system has intensified. The analysis of the possible combinations (by pairs) of G7 financial indicators showed that the largest crisis contagion problem occurs between the Italian and French banking systems, i.e.…”

On the synchronization of banking financial indexes: a wavelet-based approach

“…While applications in finance have not yet extensively utilized wavelets, there have been some interesting empirical exercises and the list is sure to grow fast. Some of the more recent closely related studies are Rua and Nunes (2009), Aguiar-Conraria and Soares (2011), Loh (2013), Dimic et al (2016), Lin et al (2018), Mandler and Scharnagl (2019) and Scharnagl and Mandler (2019), Matos, Costa and da Silva (2021). This methodology, based on Grossmann and Morlet (1984), Goupillaud et al (1984), Torrence and Compo (1998), Torrence and Webster (1999), among other authors, is well suited to our intent because it performs the estimation of the spectral characteristics of a time-series, enabling us to study both the time-varying and frequency-specific behavior of the variables.…”