CDS Spreads and Contagion Amongst Systemically Important Financial Institutions – A Spatial Econometric Approach

Abstract

This study applies a novel way of measuring, quantifying and modelling the contagion risk amongst financial institutions. The magnitude of risk spill over effects is gauged by introducing a specific weighting scheme to the regression. This approach originally stems from spatial econometrics. The methodology allows for a decomposition of the credit spread into a contagion risk premium, a systematic risk premium and an idiosyncratic risk premium. We identify considerable risk spill overs due to the interconnectedness of the financial institutes in the sample. In stress tests, up to one-fifth of the CDS spread changes are owing to financial contagion. These results also give an alternative explanation for the nonlinear relationship between a debtor's theoretical probability of default and the observed credit spreads -known as the 'credit spread puzzle'. Copyright © 2015 John Wiley & Sons, Ltd. Banks and insurance companies (henceforth financial institutions, FIs or banks) play a crucial role in an economy by exercising a liquidity transformation, risk transformation and monitoring function. However, the crisis in 2007/2008 illustrated once again that the malfunctioning of the financial system can be disastrous. Being aware of the costly consequences of financial turmoil, regulators and supervisors focused on guaranteeing the soundness of each individual FI by setting minimum capital requirements (e.g. Basel I and II, Solvency I, Swiss Solvency Test) known as microprudential regulation and supervision. With the onset of the global liquidity, credit and confidence crisis, it became obvious that microprudential regulation should be amplified by another dimension, the macro perspective. The need for such an enhancement was underscored by the origins of the financial crisis, where problems in a corner market -the subprime mortgage market, which only accounted for 3 per cent of US financial assets (Eichengreen and Sarno, 2009) -had infected the entire banking system. Eventually this jeopardised the stability of the global financial system. The fact that shocks were transmitted between seemingly independent entities and markets, denoted as contagion or spill over effects, revealed a high degree of financial interconnectedness. And the presence of spill overs across markets and banks indicates a system-inherent mechanism of shock transmission that affects all entities. This contradicts the hypothesis of a system of independent FIs. In the no contagion case, one would expect an institution's stability to relate solely to its key economic data (the fundamentals). Yet instabilities were quickly transmitted across banks in the recent crisis; we may therefore conclude that it is not only banks' fundamentals that determine their stability but also the financial system as a whole. It is vital to understand the risks and risk dynamics of a banking system in order to prevent crises.

The Time-Spatial Dimension of Eurozone Banking Systemic Risk

“…This spread captures both credit risk/banking stress and market liquidity, as well as the health of the banking system (Pelizzon et al (2016)). We expect a positive relationship with CDS spread because this difference is usually associated with economic distress, namely the spread is an indicator of the soundness of the banking system (Eder and Keiler (2015)). Therefore, the Euribor-Eonia spread is a measure of interbank funding pressure in the European Monetary Union.…”

“…By contrast, the works of Samaniego-Medina et al (2016) and Annaert et al (2013) examining the CDS spread determinants for European banks use a classic version of panel models. Second, we estimate the time-varying dynamic of contagion with respect to the papers of Eder and Keiler (2015) and Calabrese et al (2017), who used a static version of the SAR model and binary spatial autoregressive model, respectively. The distinction is important since it is unrealistic to assume that the contagion effect (spatial coefficient) is constant over the entire period.…”

Splitting Credit Risk into Systemic, Sectorial and Idiosyncratic Components

“…The model inputs were the banks' individual probabilities of default, the size of their net of capital liabilities and the banks' sensitivity to systemic factors, which capture correlations between banks' asset returns. Eder and Keiler (2015) estimated the degree of systemic risk and the magnitude of risk spillover effects by introducing a specific weighting scheme in a regression that relates observations to each other. They measure contagion effects in CDS levels as well as CDS changes.…”

Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances

“…Unfortunately, although there is large agreement about the role that the dependence structure has in finance, spatial econometric models, which generally deal with such dependences, are rarely used to solve financial problems. Notable exceptions are given by Fernandez (), who proposes the spatial capital asset pricing model (S‐CAPM), Arnold, Stahlberg, and Wied (), who investigate global and local dependencies as well as dependence effects inside industrial branches of financial returns, and Eder and Keiler () and Blasques et al (), who focus on spillover effects across financial markets using CDS data. Unlike the previous studies, our empirical investigation is based on portfolio optimization theory by exploiting the classical Markowitz () mean–variance (MV) framework.…”

“…In recent years, first efforts of employing spatial econometric techniques into financial applications have been made. Spatial spillover effects in empirical finance can take the meaning of credit risk propagation (Eder & Keiler, ), returns comovements over time (Asgharian, Hess, & Liu, ), or risk premium propagation among firms (Fernandez, ). However, most of these emerging analyses are typically based on panel data with no time‐varying spatial spillover effects.…”