Credit Spread Puzzle Research Articles

Determinants of credit default swap spread changes: The sell-side perspective

This study revisits the credit spread puzzle using credit default swap spread changes between 2002 and 2020. We find that credit-related structural variables account for only 13.8% of the variation in credit default swap spread changes. There exists a single dominant common component that explains 49.8% of the regression residuals. Sell-side risk-bearing constraints are closely related to the unknown common component and significantly improve the explanatory power of credit default swap spread changes, particularly the commonality between them.

Illiquidity in sovereign debt markets

We study sovereign debt and default policies when credit and liquidity risk are jointly determined. To account for both types of risks, we focus on an economy with incomplete markets, limited commitment, and search frictions in the secondary market for sovereign bonds. We quantify the effect of liquidity on sovereign spreads and welfare by performing quantitative exercises when our model is calibrated to match key features of the Argentinean default in 2001. From a positive point of view, we find (a) that a substantial portion of sovereign spreads is due to a liquidity premium, and (b) the liquidity premium helps to resolve the “credit spread puzzle” by generating high average spreads while maintaining a low default frequency. From a normative point of view, we find that reductions in secondary market frictions improve welfare.

Volatility morphology of asset value and credit spread puzzle

Merton model has provided a classic theoretical framework for explaining credit spreads. This paper extends Merton model by introducing morphology factor of asset value volatility in the model, and conducts empirical studies on the effect of asset volatility morphology on credit spreads in China’s bond market. The results show that asset volatility morphology is economically important and can explain credit spreads well. Furthermore, this paper analyzes the asymmetric influences of monetary policy on credit spreads and asset volatility morphology. This paper points out that the responses of credit spreads and asset volatility morphology to monetary policy are consistent in the tight liquidity environments. To this end, monetary policy and liquidity, which are two factors that have been ignored by classic Merton model but proved to have significant influences on credit spreads, play roles in influencing credit spreads by changing volatility morphology of asset value. Since asset volatility morphology can reflect the change of investors’ expectation on the default probability of asset, the argument mentioned in the credit spread puzzle that the fundamentals related to bond default probability cannot explain credit spreads needs to be reexamined.

Unemployment and credit risk

Labor market frictions help explain the credit spread puzzle. In U.S. aggregate data and newly assembled U.S. industry-level and cross-country panel datasets, the relation between unemployment and credit risk is strong and positive. In a search model of equilibrium unemployment embedded with defaultable debt and capital accumulation, search frictions create downward rigidity in expected search costs, hindering firms from repaying creditors particularly in bad times and rendering corporate debt riskier. Quantitatively, the model replicates the strongly positive relation between unemployment and credit risk as well as salient features of the credit spread, including its level, volatility, cyclicality, and skewness.

Network Risk in the European Sovereign CDS Market

This paper applies novel tools from spatial econometrics to measure, quantifyand predict sovereign CDS spreads. Network risk is modelled by making each sovereignísCDS spread a function of the CDS spreads of its ìneighborsî in the Önancial network. Themain Öndings of the paper are: (1) the network model improves forecasting accuracy by 15% to 20%; (2) exogenous Önancial shocks propagate in the network of sovereigns and 40 %to 50% of the total e§ect is due to indirect (network) e§ects. These Öndings suggest analternative explanation to the well-known credit spread puzzle. To rationalize the Öndingsthe paper develops a simple structural network model of sovereign credit risk with Önancialcross-holdings and multiple equilibria.

Bad or good neighbours: a spatial financial contagion study

PurposeThe purpose of this paper is to study the evolution of financial contagion between Eurozone banks, observing the credit default swaps (CDSs) market during the period 2009–2017.Design/methodology/approachThe authors use a dynamic spatial Durbin model that enables to explore the direct and indirect effects over the short and long run and the transmission channels of the contagion.FindingsThe results show how contagion emerges through physical and financial market links between banks. This finding implies that a bank can fail because people expect other related financial institutions to fail as well (self-fulfilling crisis). The study provides statistically significant evidence of the presence of credit risk spillovers in CDS markets. The findings show that equity market dynamics of “neighbouring” banks are important factors in risk transmission.Originality/valueThe research provides a new contribution to the analysis of EZ banking risk contagion, studying CDS spread determinants both under a temporal and spatial dimension. Considering the cross-dependence of credit spreads, the study allowed to verify the non-linearity between the probability of default of a debtor and the observed credit spreads (credit spread puzzle). The authors provide information on the transmission mechanism of contagion and, on the effects among the largest banks. In fact, through the study of short- and long-term impacts, direct and indirect, the paper classify banks of systemic importance according to their effect on the financial system.

Marking to Market Corporate Debt

Models of capital structure and credit risk make predictions about market valuations of debt, but are routinely tested on the basis of book debt from common data sources. In this paper, we propose to close this gap. We construct a rich data set on firm level debt market valuations by carefully matching data on corporate bond and loan secondary market transactions. We document significant discrepancies between market and book values, especially for distressed firms. We use our dataset to i) provide novel rules of thumb on how to adjust leverage and unlever returns using standard datasets, and ii) to revisit a number of prominent empirical patterns involving corporate debt. Using a market-based measure of Tobin's Q, we find little evidence for investment cash-flow sensitivity in our data. We find that using market debt values significantly improves default prediction, and do not detect a credit spread puzzle. In asset pricing tests, we find a leverage premium, but no evidence for a value premium after controlling for market leverage. Moreover, a novel measure of financial distress, namely market-to-book debt, predicts stock returns positively in the cross-section, inconsistent with a financial distress puzzle.

The Credit Spread Puzzle - Model-Free Evidence from a Natural Experiment

Prior literature mostly finds bond yield spreads to be insufficiently explained by credit risk in structural models (the 'credit spread puzzle'). In contrast, several recent results consider credit spreads to consist of credit risk to a substantially larger extent (if not even entirely). We address this dissent in the literature using a different empirical methodology. We utilize the removal of sovereign guarantees for savings banks and state banks in Germany as a unique quasi-natural experiment allowing identification of the credit risk component. During a transition period of over ten years, bonds of the same issuer with and without credit risk could be directly compared. Interestingly, less than 20% of the yield spread is due to credit risk for these bonds.

Is the credit spread puzzle a myth?

We revisit Feldhütter and Schaefer (FS, 2018), who report evidence of a “credit spread puzzle” for high-yield but not investment-grade bonds. We show their results are reversed when their model is calibrated to market values of debt (as required by theory) rather than book values. We then demonstrate that using credit spreads rather than historical default rates to identify the default boundary provides the statistical power necessary to reject their assumption that firm dynamics follow geometric Brownian motion. A large market price of jump risk is required to match historical default rates, which generates a credit spread puzzle for investment-grade but not high-yield bonds.

Time‐Varying Asset Volatility and the Credit Spread Puzzle

ABSTRACTMost extant structural credit risk models underestimate credit spreads—a shortcoming known as the credit spread puzzle. We consider a model with priced stochastic asset risk that is able to fit medium‐ to long‐term spreads. The model, augmented by jumps to help explain short‐term spreads, is estimated on firm‐level data and identifies significant asset variance risk premia. An important feature of the model is the significant time variation in risk premia induced by the uncertainty about asset risk. Various extensions are considered, among them optimal leverage and endogenous default.

Do Information Contagion and Business Model Similarities Explain Bank Credit Risk Commonalities?

This paper revisits the credit spread puzzle in bank CDS spreads from the perspective of information contagion. The puzzle, first detected in corporate bonds, consists of two stylized facts: Structural determinants of credit risk not only have low explanatory power but also fail to capture common factors in the residuals (Collin-Dufresne et al., 2001). For the case of banks, we hypothesize that the puzzle exists because of omitted network effects. We therefore extend the structural models to account for information spillovers based on bank business model similarities. To capture this channel, we propose and construct a new intuitive measure for portfolio overlap using the complete asset holdings of the largest banks in the Eurozone. Incorporating the network information into the structural model for bank credit spreads increases explanatory power and removes a systemic common factor as well as a North-South common factor from the residuals. Furthermore, neglecting the network likely overstates the importance of structural determinants.

Is the Credit Spread Puzzle a Myth?

Structural models of default can identify asset value dynamics and the location of the default boundary from either (observable) spreads or (latent) default probabilities. The latter approach uses historical default rates as proxies, which provide such low statistical power that assumptions regarding asset value dynamics (e.g., geometric Brownian motion) typically go untested. In contrast, calibration via spreads offers much more power, and clearly detects jumps in (risk-neutral) asset value dynamics. A large market price of risk on jumps is required to match historical default rates, thus reaffirming a credit spread puzzle for investment-grade but not high-yield bonds.

An endogenous structural credit risk model incorporating with moral hazard and rollover risk

In this paper, we embed optimal contracting between the manager and equity holders into Leland-Toft endogenous structural credit risk model to study the impact of moral hazard on the firm's credit risk with rollover debts. Our model quantitatively shows that the agency costs induced by the moral hazard can endogenously have significant impacts on credit spreads, besides the costs of rolling over the maturing debts of the firm. It originates from the conflicts that these two costs should be covered by equity holders while both the manager and maturing debt holders are still paid in full. The numerical results show that the credit spread with the agency costs of moral hazard is larger than the one without the agency costs. Thus, the moral hazard could be used to explain “credit spread puzzle” as an endogenous factor. The explicit formulae of the equity value, the debt value, and the endogenous default boundary are also given.

Illiquidity in Sovereign Debt Markets

We study sovereign debt and default policies when and liquidity risk are jointly determined. To account for both types of risks we focus on an economy with incomplete markets, limited commitment, and search frictions in the secondary market for sovereign bonds. We quantify the effect of liquidity on sovereign spreads and welfare by performing quantitative exercises when our model is calibrated to match key features of the Argentinean default in 2001. From a positive point of view, we find (a) that a substantial portion of sovereign spreads is due to a liquidity premium, and (b) the liquidity premium helps to resolve the credit spread puzzle, by generating high mean spreads while maintaining a low default frequency. From a normative point of view, we find that reductions in secondary market frictions improve welfare.

The Myth of the Credit Spread Puzzle

We ask whether a standard structural model (Black and Cox, 1976) is able to explain credit spreads on corporate bonds and, in contrast to much of the literature, we find that the model matches the level of investment grade spreads well. Model spreads for speculative grade debt are too low and we find that bond illiquidity contributes to this underpricing. Our analysis makes use of a new approach for calibrating the model to historical default rates that leads to much more precise estimates of investment grade default probabilities.

Time-Varying Asset Volatility and the Credit Spread Puzzle

Time-Varying Asset Volatility and the Credit Spread Puzzle

Idiosyncratic Tail Risk and the Credit Spread Puzzle

This paper studies the asset pricing implications of idiosyncratic labor income tail risk on credit spread. I propose a model featuring an incomplete market, heterogeneous households with recursive preference, and comovement of tail risk in labor income and firm cash flow growth. The model produces strong covariation of households' marginal utility and default rates, which helps to explain the stylized fact that the credit spread (1) is on average large and (2) is positively related to labor tail risk. Quantitatively, the tail risk premium can account for as much as 68% of the observed credit spread. My framework provides a new insight, drawn from an option perspective, that the implications of idiosyncratic tail risk for stocks and bonds can be very different.

Do Information Contagion and Business Model Similarities Explain Bank Credit Risk Commonalities?

This paper revisits the credit spread puzzle in bank CDS spreads from the perspective of information contagion. The puzzle, first detected in corporate bonds, consists of two stylized facts: Structural determinants of credit risk not only have low explanatory power but also fail to capture common factors in the residuals (Collin-Dufresne et al., 2001). For the case of banks, we hypothesize that the puzzle exists because of omitted network effects. We therefore extend the structural models to account for information spillovers based on bank business model similarities. To capture this channel, we propose and construct a new intuitive measure for portfolio overlap using the complete asset holdings of the largest banks in the Eurozone. Incorporating the network information into the structural model for bank credit spreads increases explanatory power and removes a systemic common factor as well as a North-South common factor from the residuals. Furthermore, neglecting the network likely overstates the importance of structural determinants.

The composition of CMBS risk

Abstract This paper identifies the put-option, liquidity availability proportion, and shadow liquidity risk premia embedded within commercial mortgage backed securities (CMBS) using reduced form and structural generalization models. These risk values are then interpreted as trading signals which are tested with automated trading strategies that buy undervalued and sell overvalued CMBS from November 2007 through June 2015. All three signals generate substantial positive trading profits in testing for the reduced form model but not for the structural generalization. The risk signals constructed independently of market pricing provide more profitable automated trading insights than those constructed from interactions between modeled risk measures and market spreads. In my tests of the information content of the risk signals with respect to future macroeconomic indicators, I find statistically significant evidence in keeping with recent studies. While I cannot reject CMBS efficiency, this paper’s disclosure of new risk measures, the profitability of automated strategies based on those risk measures, and the statistical significance of their forward guidance capabilities, together contributes to our understanding of CMBS risk and the credit spread puzzle debate.

Can Higher-Order Risks Explain the Credit Spread Puzzle?

We tweak the conventional Merton model to account for the asymmetric properties of assets returns and investors asymmetric behavior toward the upside potential of gain versus the downside risk of loss. Using an asymmetric split normal distribution, we capture empirical asymmetries in the underlying return distribution, while we conserve the attractiveness of delivering closed-form pricing formulas that collapse to the basic Merton model in the symmetric Gaussian case. The asymmetric specification outperforms the symmetric one in matching high levels of historical credit spreads. We then link the residual (non-default-model-implied) spread to two illiquidity risk factors. The first factor is extracted from several measures of idiosyncratic illiquidity variables and the second factor is a systematic factor obtained from a general index common to all studied bonds. Our model explains 70% of the BBB-AAA spread and more than 72% of BBB and AAA credit spreads relative to the on-the-run Treasury rates.