Bernd Schwaab

Conditional Euro Area Sovereign Default Risk

We propose an empirical framework to assess the likelihood of joint and conditional sovereign default from observed CDS prices. Our model is based on a dynamic skewed-t distribution that captures all salient features of the data, including skewed and heavy-tailed changes in the price of CDS protection against sovereign default, as well as dynamic volatilities and correlations that ensure that uncertainty and risk dependence can increase in times of stress. We apply the framework to euro area sovereign CDS spreads during the euro area debt crisis. Our results reveal significant time-variation in distress dependence and spill-over effects for sovereign default risk. We investigate market perceptions of joint and conditional sovereign risk around announcements of Eurosystem asset purchases programs, and document a strong impact on joint risk.

Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter estimation using standard maximum likelihood methods. We adopt the new framework for signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moodys-rated firms from January 1982 to March 2010.

Systemic Risk Diagnostics: Coincident Indicators and Early Warning Signals

We propose a novel framework to assess financial system risk. Using a dynamic factor framework based on state-space methods, we construct coincident measures ( JEL Classification: G21, C33

DYNAMIC FACTOR MODELS WITH MACRO, FRAILTY, AND INDUSTRY EFFECTS FOR U.S. DEFAULT COUNTS THE CREDIT CRISIS OF 2008

We develop a high-dimensional, nonlinear, and non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into latent components for (1) macroeconomic/financial risk, (2) autonomous default dynamics (frailty), and (3) industry-specific effects. We analyze discrete U.S. corporate default counts together with macroeconomic and financial variables in one unifying framework. We find that approximately 35% of default rate variation is due to systematic and industry factors. Approximately one-third of this systematic variation is captured by the macroeconomic and financial factors. The remainder is captured by frailty (40%) and industry (25%) effects. The default-specific effects are particularly relevant before and during times of financial turbulence. We detect a build-up of systematic risk over the period preceding the 2008 credit crisis. This article has online supplementary material.

Modeling Financial Sector Joint Tail Risk in the Euro Area

Two new measures for financial systemic risk are computed based on the time-varying conditional and unconditional probability of simultaneous failures of several financial institutions. These risk measures are derived from a multivariate model that allows for skewed and heavy-tailed changes in the market value of financial firms’ equity. Our model can be interpreted as a Merton model with correlated Lévy drivers. This model incorporates dynamic volatilities and dependence measures and uses the overall information on the shape of the multivariate distribution. Our correlation estimates are robust against possible outliers and influential observations. For very large crosssectional dimensions, we propose an approximation based on a conditional Law of Large Numbers to compute extreme joint default probabilities. We apply the model to assess the risk of joint financial firm failure in the European Union during the financial crisis.We develop a novel high-dimensional non-Gaussian modeling framework to infer measures of conditional and joint default risk for numerous financial sector firms. The model is based on a dynamic Generalized Hyperbolic Skewed-t block-equicorrelation copula with time-varying volatility and dependence parameters that naturally accommodates asymmetries, heavy tails, as well as non-linear and time-varying default dependence. We apply a conditional law of large numbers in this setting to define joint and conditional risk measures that can be evaluated quickly and reliably. We apply the modeling framework to assess the joint risk from multiple defaults in the euro area during the 2008-2012 financial and sovereign debt crisis. We document unprecedented tail risks between 2011-2012, as well as their steep decline following subsequent policy actions. JEL Classification: G21, C32

Conditional Probabilities for Euro Area Sovereign Default Risk

We propose a novel empirical framework to assess the likelihood of joint and conditional failure for Euro area sovereigns. Our model is based on a dynamic skewed-t copula which captures all the salient features of the data, including skewed and heavy-tailed changes in the price of CDS protection against sovereign default, as well as dynamic volatilities and correlations to ensure that failure dependence can increase in times of stress. We apply the framework to Euro area sovereign CDS spreads from 2008 to mid-2011. Our results reveal significant time-variation in risk dependence and considerable spill-over effects in the likelihood of sovereign failures. We also investigate distress dependence around a key policy announcement by Euro area heads of state on May 9, 2010, and demonstrate the importance of capturing higher-order time-varying moments during times of crisis for the correct assessment of interacting risks.

Macro, Frailty, and Contagion Effects in Defaults: Lessons from the 2008 Credit Crisis

In the aftermath of the financial crisis, banks have been subjected to a sequence of stress tests to measure system stability. Such tests are formulated in terms of adverse economic scenarios rather than in terms of systematic default rate increases.This suggests that macroeconomic conditions fully capture default stress. However, two additional explanations can be found in the literature for the occurrence of default clusters: autonomous default rate dynamics, also known as frailty, and industry-specific effects including contagion. We develop a new methodological framework to disentangle, quantify, and test these three competing explanations. Using U.S. default data we find that observed macro and financial market factors account for only 30--60% of systematic default risk. Consequently, stress-testing frameworks that only control for observed macro conditions leave out a substantial share of systematic risk. The components not related to business-cycle dynamics (frailty) are particularly relevant before and during times of financial market turbulence. For example, we find clear systemic risk build-up over the period preceding the 2008 credit crisis.

A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-Standard Monetary Policy in the Euro Area

We develop an econometric methodology for the study of the yield curve and its interactions with measures of non-standard monetary policy during possibly turbulent times. The yield curve is modeled by the dynamic Nelson-Siegel model while the monetary policy measurements are modeled as non-Gaussian variables that interact with latent dynamic factors, including the yield factors of level and slope. Yield developments during the financial and sovereign debt crises require the yield curve model to be extended with stochastic volatility and heavy tailed disturbances. We develop a flexible estimation method for the model parameters with a novel implementation of the importance sampling technique. We empirically investigate how the yields in Germany, France, Italy and Spain have been affected by monetary policy measures of the European Central Bank. We model the euro area interbank lending rate EONIA by a log-normal distribution and the bond market purchases within the ECBs Securities Markets Programme by a Poisson distribution. We find evidence that the bond market interventions had a direct and temporary effect on the yield curve lasting up to ten weeks, and find limited evidence that purchases changed the relationship between the EONIA rate and the term structure factors.

Bank and Sovereign Credit Risk: Spillover Effects from the ECB's Comprehensive Assessment

We study spillovers from bank to sovereign risk in the euro area using difference specifications around the European Central Banks release of stress test results for 130 significant banks on October 26, 2014. We document that following this information release bank equity prices in stressed countries declined. Surprisingly, bank risk in stressed countries was not absorbed by their sovereigns but spilled over to non-stressed euro area sovereigns. As a result, in non-stressed countries, the co-movement between sovereign and bank risk increased. This suggests that market participants perceived that bank risk is shared within the euro area.We quantify the transmission of risk from the banking to the sovereign sector within and across borders in the euro area. Our empirical findings are based on difference and difference-in-differences specifications around the European Central Bank’s (ECB) release of the outcome of its Comprehensive Assessment (CA) of the 130 most significant banks in the euro area, on 26 October 2014. An associated information shock in stressed countries led to a reassessment of bank risk and, as a consequence, of sovereign risk. Surprisingly, we find that there is no risk transmission from domestic banks to their respective sovereign in stressed countries. Instead, non-stressed countries bear the risk by providing guarantees to banks in stressed countries. This implies that the well-known bank-sovereign nexus has an important cross-border component. JEL classification: C68, G15, F34.

Bank Business Models at Zero Interest Rates

We propose a novel observation-driven finite mixture model for the study of banking data. The model accommodates time-varying component means and covariance matrices, normal and Student’s t distributed mixtures, and economic determinants of time-varying parameters. Monte Carlo experiments suggest that units of interest can be classified reliably into distinct components in a variety of settings. In an empirical study of 208 European banks between 2008Q1–2015Q4, we identify six business model components and discuss how their properties evolve over time. Changes in the yield curve predict changes in average business model characteristics.