Taxes, Default Risk, and Credit Spreads

Credit Risk and Risk Neutral Default Probabilities: Information About Rating Migrations and Defaults

Cross-Market and Cross-Firm Effects in Implied Default Probabilities and Recovery Values

Evidence of Personal Tax Effects on the Pricing of Corporate Bonds

The Aggregate Consequences of Default Risk: Evidence from Firm-Level Data

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.

This paper represents an extension and integration of recent empirical and theoretical research on default risk and taxability. The purpose of the paper is to develop and test a model of interest rate spreads which incorporates both the effect of taxes and differences in default probabilities in a theoretically correct manner. There is an important fundamental difference between our approach to explaining yield spreads and the approach most commonly taken in literature. Unlike nearly all of the previous work, we do not begin with a yield spread model, i.e., one which begins by examining differences in yields, but rather begin with an expected return or pricing model, which can then be expressed in the yield spread format. This is a fundamental difference in approaches which we feel leads to a superior theoretical formulation which can then be tested empirically without many of the problems inherent in the alter- native approach. The theoretical model is a simple extension of earlier work on default by While there is a considerable literature that analyzes the effect of taxability on rate spreads, we are unaware of any previous study that considers tax conse- quences in the event of default, a rather surprising omission.

We develop a structural credit model to examine how interactions between default and liquidity affect corporate bond pricing. The model features debt rollover and bond-price-dependent holding costs. Over the business cycle and in the cross-section, the model matches average default rates and credit spreads in the data, and captures variations in bid-ask and bond-CDS spreads. A structural decomposition reveals that default-liquidity interactions can account for 10%–24% of the level of credit spreads and 16%–46% of the changes in spreads over the business cycle. Further, liquidity-related corporate bond financing costs amount to 6% of the total issuance amount from 1996 to 2015. Received July 12, 2015; editorial decision April 15, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press website next to the link to the final published paper online.

Probability of default (PD) estimation is essential to the calculation of expected credit loss under the Basel III framework and the International Financial Reporting Standard 9. Gross domestic product (GDP) growth has been adopted as a key determinant in PD estimation models. However, PD models with a GDP covariate may not perform well under aberrant (i.e., outlier) conditions such as the COVID-19 pandemic. This study explored the robustness of a PD model with a GDP determinant (the test model) in comparison with that of a PD model with a credit default swap index (CDX) determinant (the alternative model). The test model had a significantly greater ratio of increase in Akaike information criterion than the alternative model in comparisons of the fit performance of models including 2020 data with that of models excluding 2020 data (i.e., that do not cover the COVID-19 pandemic). Furthermore, the Cook’s distance of the 2020 data of the test model was significantly greater than that of the alternative model. Therefore, the test model exhibited a serious robustness issue in outlier scenarios, such as the COVID-19 pandemic, whereas the alternative model was more robust. This finding opens the prospect for the CDX to potentially serve as an alternative to GDP in PD estimation models.

Market conditions, default risk and credit spreads

The effection of liquidity on the financial risk is becoming more and more important. Ignoring it the risk analysis would be in failure. This paper provides a default-risky debt valuation model, which assumes that market liquidity modelled by the intensity of default is driven by a continuous-time Markov chain. The model accounts for default probability with liquidity risk calibrated. A semimartingale representation of a liquid defaultable debt price is obtained. The illiquidity is modelled as exogenously specified stochastic reduction in the price of the debt, which adds more risks for the investors.

Rating agencies claim to look through the cycle when assigning corporate credit ratings, which entails that they are able to separate trend components of default risk from transitory ones. To test whether agencies possess this competence, I take market-based estimates of 1-year default probabilities of corporate bond issuers and estimate their long-run trend using the Hodrick-Prescott filter, local regression, or centered moving averages. I find that ratings help identify the current split into trend and cycle. In addition, rating stability is similar to the one of hypothetical ratings based on long-term trends. The results are robust to the use of different filter techniques. They are confirmed by a model-free analysis, which shows that ratings predict future changes in market-based default probability estimates. Since the examined trends are forward-looking in the sense that the trend filtering algorithms use future data, agency ratings exhibit important characteristics one would expect from ratings that see through the cycle.

Corporate yield spreads and real interest rates

Abstract. The objective of this article is theoretical and methodological justifying of determining algorithm of the cost of debt capital for enterprises functioning in emerging markets (EM). The methods of research: analysis and synthesis, system analysis, comparative analysis, empirical and statistical methods, factor analysis. Results. In this article key determinants of interest rates on debt capital for enterprises and their impact on the procedure of discount rate calculation are determined. The issue of the cost of debt calculation of enterprises in condition of absence of complete information concerning systematic and non-systematic crediting risks is studied. Differences between interest rate on the loan fixed in credit agreement and expected by creditors the cost of debt are identified. It is determined that the key factor impacting the deviation level of market value of debt capital from the nominal, and respectively, deviation of the cost of debt from the cost of capital is probability of default (PD). At the minimum values of PD, the contract interest rate corresponds to the rate of cost of debt and it is advisable to use it for discount rate calculation. Critical analysis of alternative methodological approaches of the cost of debt calculation is made. Ways of integrating of market information concerning credit default swaps into the process of expected cost of debt calculation are justified. Factors of shadowing of rates of the cost of debt and ways of reducing of shadow transactions’ level in the credit market are identified. Conclusions. At high PD values, expected by market premium for default risk may exceed the contract interest rate, which necessitates constant monitoring of credit risks and appropriate adaptation of interest rates. In the paper the algorithm of such adaptation are proposed. It is shown that in the case of non-use of interest rates adjustment taking into account changes in PD, CDS and LGD, premium for creditors’ systematic risk can differ significantly from market values of similar enterprises (peer-group), and estimated value of the cost of debt can acquire negative values. Contract (promised) interest rate should be set in such way that the premium for systematic risk of providing debt capital will be at the level of similar companies and does not change significantly as a result of probability of default changes. If in practice the opposite situation occurs, it is the evidence of contract interest rate shadowing, absence of effective system of assessment and management of credit risks. For solving the problem of interest rate transparency and filling of information gaps concerning PD borrowers in EM countries, should intensify CDS market. Keywords: debt capital, default probability, non-performing loans, credit default swap, credit spread, debt capital premium, shadow economy. JEL Classification E47 Formulas: 16; fig.: 0; tabl.: 3; bibl.: 15.

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.

This paper contributes to the burgeoning literature on the so-called “risk-taking channel” of monetary policy by constructing a partial equilibrium model of the commercial loan market, where conventional monetary policy influences bank lending standards and, by extension, the risk of bank default. In the model, borrower heterogeneity in terms of entrepreneurial abilities and project revenue results in optimal commercial bank lending standards. These standards take the form of a minimum ability requirement: only borrowers with abilities greater than or equal to the minimum threshold receive a loan; the rest are completely excluded from the loan market. After loans are made, a negative aggregate shock that lowers all borrowers’ project revenue results in unexpected loan losses for the bank that may push it into default. In the baseline model, lending standards loosen and the probability of bank default rises when the central bank’s policy rate falls, a result that suggests that asset quality and lending standards may play an important role in shaping the risk-taking channel of monetary policy. However, the relationship between bank default and the policy rate is sensitive to the extent of bank capitalization. While default risk and the policy rate are negatively correlated at relatively strongly capitalized banks, the risk of default at more weakly capitalized banks increases with the policy rate. Additionally, default risk at strongly capitalized banks is more responsive to changes in the policy rate than at weakly capitalized banks. This paper contributes to the literature on the risk-taking channel of monetary policy. First, the model enables analysis of the impact of monetary policy on risk-taking in a setting where both bank asset quality and leverage play a role in shaping the risk of bank default. Second, the model’s results on the role of bank capitalization imply that the tradeoff between financial stability and monetary policy depends crucially on bank capital ratios. This suggests that recent changes to bank capital requirements embodied in new post-2008 financial crisis regulations may impact the nature and size of the risk-taking channel.