Systemic Credit Risk Research Articles

Capital regulation induced reaching for systematic yield: Financial instability through fire sales

Credit rating-based capital regulation induces financial institutions to take on additional systematic risk. In this paper, we uncover interconnected channels through which this systematic risk hoarding affects financial stability using a proprietary ECB bond holdings dataset. First, banks and insurance corporations effectively reduce their capital buffers by hoarding bonds with high systematic credit risk. Second, this hoarding increases the portfolio concentration of credit rating-constrained and unconstrained financial institutions. Third, in addition to the general tendency of regulated financial institutions to fire sale bonds after rating downgrades, we reveal even larger fire sales precisely when their regulatory advantages of reaching for systematic yield disappear. Using a shock in capital regulation, we establish this causal relationship between the severity of fire sales and the tendencies of regulatory-constrained financial institutions to seek bonds with high systematic credit risk. Such systematic risk hoarding reduces capital buffer by an additional 16% in economic downturns.

Inclusion of debt claims in asset pricing models: Evidence from the CDS Index

Asset pricing theory suggests that the correct proxy for the market portfolio should contain both the debt and equity claims of the economy, whereas prevailing empirical studies fail to include the debt claim. Motived by the discrepancy between the theoretical and empirical models and the difficulty in constructing proxies, the study uses the Credit Default Swaps (CDS) market index as a proxy for the debt market and empirically tests its explanatory power in explaining stock return variations. Employing panel regression and Fama-MacBeth regression of all publicly traded U.S. companies from 2005 to 2020, the study finds a negative relationship between CDS index returns and stock returns. On average, a one standard deviation increase in CDS index return is associated with a 0.02% decrease in daily stock returns. Results of two-stage regressions show that the estimated systematic credit risk is positively priced in stock returns with similar economic magnitude as the well-documented beta risk. These results support asset pricing theories in the inclusion of debt claim and the risk-return tradeoff, while contradicting the credit risk puzzle documented in prior studies.

European systemic credit risk transmission using Bayesian networks

The analysis of systemic credit risk is one of the most important concerns within the financial system. Its complexity lies in adequately measuring how the transmission of systemic default spreads through assets or financial markets. The transmission structure of systemic credit risk across several European sectoral CDS is studied by dynamic Bayesian networks. The new approach allows for a more advanced analysis of systemic risk transmission, including long-term and more complex relationships. The modelling reveals as relevant only relationships between the original series and one- and two-lagged series. Network structure learning displays a robust and stationary underlying risk transmission structure, pointing to a consolidated transmission mechanism of systemic credit risk between CDSs. Between 5 % and 40 % of sectoral CDS series variances are explained by the network relationships. The modelling allows us to ascertain which relationships between the CDS series show positive (amplifier) and negative (reducer) effects of systemic risk transmission.

Doing Well While Doing Good: The Elusive Quest for Green Bond Returns

In order to facilitate the transition to a low-emissions economic growth model, a burgeoning new “green finance” market has developed, providing ample opportunities for increasingly sustainability-focused investors. Using data for US and euro investment-grade corporate bond universes, this article seeks to analyze and quantify the investment relevance of green bond labels. The available empirical evidence points to three tentative conclusions: (i) labeled bonds tend to trade with slightly tighter spreads than conventional counterparts; however, this turns out not to matter for explaining total returns once typical measures of systematic credit risk are taken into account; (ii) contrary to common assertions, green bonds overall did not weather the COVID-induced sell-off in March 2020 significantly better than nongreen peers, and differences in performance between select green indexes and wider market benchmarks are largely attributable to compositional effects; (iii) EUR and USD green bond portfolios can be optimized to match the risk profiles of broad market benchmarks, while outperforming them in risk-adjusted terms.

Do Investors Trade Industry Sector-Based Credit Risk Differently Than Systematic Credit Risk?

The authors examine price discovery and informational flow between the industry sector-based subindices of the Markit CDX.NA.IG Index and matched portfolios of stocks to ascertain whether they differ from the composite index and one another, possibly due to differing business risk and exposure to systematic factors or investor preferences. They find that although neither market leads the other in pricing in new information in the systematic markets, sector-based matched equity portfolios persistently lead CDS subindices in capturing this information. The only exception is the technology, media, and telecommunications sector, where they observe a two-way interactive effect. They attribute the effect to the comparatively greater growth prospects in this sector and related investor attention. These findings suggest that investors may trade sector-based CDS indices very differently than systematic indices. <b>Key Findings</b> ▪ Price discovery and informational flow between the industry sector-based subindices of the Markit CDX.NA.IG Index and matched portfolios of stocks differs from the composite index as well as within the sectors themselves. Investors trade sector-based CDS indices very differently than systematic indices, a finding with implications for stakeholders who use CDS indices for their informational content. ▪ Although neither market leads the other in pricing in new information in the systematic markets, four of the five sector-based matched equity portfolios lead their respective CDS subindices in capturing this information. ▪ There is a two-way interactive information flow within the technology, media, and telecommunications sector, suggesting more investor interest relative to the other sectors.

The Nonlinear Dynamics of Corporate Bond Spreads: Regime-Dependent Effects of their Determinants

Abstract This paper studies the behavior of corporate bond spreads during different market regimes between 2004 and 2016. Applying a Markov-switching vector autoregressive (MS-VAR) model, we document that the dynamic impact of spread determinants varies substantially with market conditions. In periods of high volatility, systematic credit risk—rather than interest rate movements—contributes to driving up spreads. Moreover, while market-wide liquidity risk is not priced when volatility is low, it becomes a crucial factor during stress periods. Our results challenge the notion that spreads predominantly capture credit risk and suggest it must be reassessed during periods of financial distress.

Jednofaktorski model za stope neizmirenja po tipovima kredita

This paper investigates the link between default rates by loan types and the systemic credit risk component. This link is described by a linear model that combines systemic and idiosyncratic contributions. The systemic component is a latent factor that depends directly on the aggregate loan default rate, while the idiosyncratic component drives specific variations of default rates across loan types. By transforming observable risk measures, the model can be econometrically represented as a mixed-effects model, where the systemic and idiosyncratic components represent, respectively, the slope and the intercept that are specific for each loan type individually. The proposed model is illustrated on a panel of defaulted loans of the Association of Serbian Banks. The obtained results show the model's very high power in explaining average default rates for all loan types. Thus, the aggregate default rate plays the role of a unique systemic component that mimics the influence of fundamental macroeconomic risk factors easily, without the necessity to model this relationship explicitly.

Systematic credit risk in securitised mortgage portfolios

This study analyses the level of systematic risk for US mortgage portfolio securitisations based on the variation of default rates which cannot be explained by observed deterministic factors. Systematic risk is decomposed into general systemic risk, rating-class-specific systematic risk and their covariance structure. General systematic risk sensitivities increase from lower rating classes to medium rating classes and decreases to higher rating classes. Rating-class-specific systematic risk shows an opposite pattern. The methodology provides for more accurate probability of default and Value-at-Risk forecasts.

Modeling Repayment Behavior of Consumer Loan in Portfolio across Business Cycle: A Triplet Markov Model Approach

With a view to develop a more realistic model for credit risk analysis in consumer loan, our paper addresses the problem of how to incorporate business cycles into a repayment behavior model of consumer loan in portfolio. A particular Triplet Markov Model (TMM) is presented and introduced to describe the dynamic repayment behavior of consumers. The particular TMM can simultaneously capture the phases of business cycles, transition of systematic credit risk of a loan portfolio, and Markov repayment behavior of consumers. The corresponding Markov chain Monte Carlo algorithms of the particular TMM are also developed for estimating the model parameters. We show how the transition of consumers’ repayment states and systematic credit risk of a loan portfolio are affected by the phases of business cycles through simulations.

Looking through systemic credit risk: Determinants, stress testing and market value

We provide a methodology to estimate a Global Credit Risk Factor (GCRF) from CDS spreads using the information provided by the default-related component of observed spreads. These are previously estimated using Pan and Singleton (2008) methodology. The estimated factor contains higher explanatory power on CDS spread fluctuations across sectors than standard credit indices like iTraxx or CDX. We find a positive association between GCRF and implied volatility variables, and a negative association with MSCI stock market sector indices as well as with interest rates and with the slope and the curvature of the term structure. Such correlations provide useful insights for risk management as well as for the hedging of credit portfolios. Indeed, we present a synthetic factor regression model for GCRF that we apply in a stress testing methodology for credit portfolios as well as to evaluate future credit risk scenarios. Finally, we show evidence suggesting that the exposure to systemic credit risk was priced in the market during the 2006–2015 period.

Credit Risk Contagion and Systemic Risk on Networks

This paper proposes a model of the dynamics of credit contagion through non-performing loans on financial networks. Credit risk contagion is modeled in the context of the classical SIS (Susceptibles-Infected-Susceptibles) epidemic processes on networks but with a fundamental novelty. In fact, we assume the presence of two different classes of infected agents, and then we differentiate the dynamics of assets subject to idiosyncratic risk from those affected by systemic risk by adopting a SIIS (Susceptible-Infected1-Infected2-Susceptible) model. In the recent literature in this field, the effect of systemic credit risk on the performance of the financial network is a hot topic. We perform numerical simulations intended to explore the roles played by two different network structures on the long-term behavior of assets affected by systemic risk in order to analyze the effect of the topology of the underlying network structure on the spreading of systemic risk on the structure. Random graphs, i.e., the Erdös–Rényi model, are considered “benchmark” network structures while core-periphery structures are often indicated in the literature as idealized structures, although they are able to capture interesting, specific features of real-world financial networks. Moreover, as a matter of comparison, we also perform numerical experiments on small-world networks.

M‐PRESS‐CreditRisk: Microprudential and Macroprudential Capital Requirements for Credit Risk under Systemic Stress

AbstractM‐PRESS‐CreditRisk is a novel stress testing approach that can help authorities gauge banks' capital adequacy related to credit risk. For the first time, it combines the assessment of microprudential capital requirements under Pillars 1 and 2 and macroprudential buffers in a unified, coherent framework. Its core element is an advanced credit portfolio model—SystemicCreditRisk—built upon a rich, nonlinear dependence structure for correlated bank portfolios. The model is applied to a sample of 12 systemically important German banking groups and delivers measures for systemic credit risk and the banks' contributions to it in both baseline and stress scenarios.

What Drives Systemic State Credit Risk? Evidence from the State Credit Default Swap (CDS) Market

In this article, using state credit default swap data, the authors examine the role of common macroeconomic factors in driving systemic sovereign credit risk. Using a structural model with jump risk, the authors demonstrate that co-movement in state economic fundamentals is an important channel of systemic credit risk. Empirical evidence shows that changes in macroeconomic fundamentals explain more variations in state credit spread and its systemic component than do financial market variables. This evidence points to macroeconomic linkages, not financial linkages, as the leading source of systemic state credit risk and suggests that this risk is driven mainly by weakness in economic fundamentals.

The Nonlinear Dynamics of Corporate Bond Spreads: Regime-Dependent Effects of Their Determinants

This paper studies the behavior of corporate bond spreads during different market regimes between 2004 and 2016. Applying a Markov-switching vector autoregressive (MS-VAR) model, we document that the dynamic impact of spread determinants varies substantially with market conditions. In periods of high volatility, systematic credit risk - rather than interest rate movements - contributes to driving up spreads. Moreover, while market-wide liquidity risk is not priced when volatility is low, it becomes a crucial factor during stress periods. Our results challenge the notion that spreads predominantly capture credit risk and suggest it must be reassessed during periods of financial distress.

What Drives Systemic Credit Risk? Evidence from the US State CDS Market

The root cause of systemic risk is an issue of on-going debate. We document evidence that common shocks of macroeconomic fundamentals are key driver of US state systemic credit risk. A structure model is developed to show importance of economic fundamentals. We find that macroeconomic variables have higher explanatory power for the temporal variation in state credit spreads and its systemic component than do financial market variables. Empirical evidence points to the macroeconomic linkages, not financial linkages, as the dominating source of systemic credit risk, and suggests that state credit risk is mainly driven by the weakness in economic fundamentals.

The joint credit risk of UK global‐systemically important banks

We study the joint credit risk in the UK banking sector using the weekly CDS spreads of global systemically important banks over 2007–2015. We show that the time‐varying and asymmetric dependence structure of the CDS spread changes is closely related to the joint default probability that two or more banks simultaneously default. We are able to flexibly measure the joint credit risk at the high‐frequency level by applying the combination of the reduced‐form model and the GAS‐based dynamic asymmetric copula model to the CDS spreads. We also verify that much of the dependence structure of the CDS spread changes are driven by the market factors. Overall, our study demonstrates that the market factors are key inputs for the effective management of the systemic credit risk in the banking sector.

Systematic Credit Risk: CDX Index Correlation and Extreme Dependence

Dependence is an important issue in credit risk portfolio modeling and pricing. We discuss a straightforward common factor model of credit risk dependence, which is motivated by intensity models such as Duffie and Singleton (1998), among others. In the empirical analysis, we study dependence under the risk-neutral measure using credit default swap (CDS) spread data of liquid large-cap U.S. obligors. The proxy for the commonfactor is the DJ CDX.NA.IG index. We document that (i) the CDX factor is significant but has low explanatory power, (ii) factor sensitivities show distinct time-varying nature and that (iii) systematic credit risk shows asymmetric extreme factor dependence, where extreme dependence is present for upward CDX movements only. This finding from an EVT-copula approach is what is predicted by various intensity models of joint defaults.

Credit risk transfer in SME loan guarantee networks

Joint loan guarantee contracts and mutual guarantee contracts among SMEs form the basis of SME guarantee networks. The expansion of these networks increases the fragility of a financial system as a result of the regional and industrial risk contagion embedded within them. By providing a theoretical framework of a loan guarantee network, a method is proposed for calculating the amount of risk spillover caused by loan guarantees taking the perspective of the entire network. In addition, the route of risk contagion in guarantee networks is analyzed, revealing that when default risk shocks occur, risk contagion travels along the nodes not once but for several rounds and that the risk control of one firm cannot prevent these systemic risks. Therefore, a risk control scheme is designed based on the location and importance of firms in the network. Using data from a real guarantee network, we demonstrate that identifying the node locations of firms’ in the guarantee network (including the coritivity and closeness of the firm) can help in understanding risk contagion mechanisms and preventing systemic credit risk before a crisis occurs.

Systemic Risk and Credit Risk in Bank Loan Portfolios

Systemic Risk and Credit Risk in Bank Loan Portfolios

M-Press-CreditRisk: A Holistic Micro- and Macroprudential Approach to Capital Requirements

M-PRESS-CreditRisk is a new top-down macro stress testing framework that can help supervisors gauge banks' capital adequacy related to credit risk. For the first time, it combines calibration of microprudential capital requirements and macroprudential buffers in a unified, coherent framework. Its core element is an advanced credit portfolio model - SystemicCreditRisk - built upon a rich, non-linear dependence structure for interconnected bank portfolios. Incorporating numerous sector/country-specific systematic factors, the model focuses on credit default concentration risk as a major source of large losses that may have systemic impact. A test run using a sample of 12 systemically important German banks provides measures for systemic credit risk and the banks' contributions to it in both baseline and stress scenarios. Capital requirements calibrated to the results combine elements of Pillar 1 and Pillar 2, whereas macroprudential buffers can internalize the system's tail risk. The maximum model-based combined requirements range between 6.3% and 27.2% of credit RWA depending on the bank. A comparison with the reported capital figures suggests that there appears to be enough capital in the banking system, but its distribution might be suboptimal from a systemic point of view as the capital level of a number of banks might need improvement.