Economic links and credit spreads

Counterparty risk is an important determinant of corporate credit spreads. However, there are only a few techniques available to isolate it from other factors. In this paper we describe a model of financial networks that is suitable for the construction of proxies for counterparty risk. Using data on North American supplier–customer network of public companies, we find that, for each supplier, counterparties’ leverage and option implied volatilities are significant determinants of corporate credit spreads in the period after the 2008–2009 U.S. recession. Our findings are robust after controlling for several idiosyncratic, industry, and market factors.     

The search for relative value in bonds

Comparing asset swap spreads across bonds is a widely used tool for measuring relative value. This approach leads portfolio managers to increase their risk exposure in ways that are not transparent. Credit default swaps are utilized to demonstrate that viewing wide asset swaps as an indicator of relative value is a mirage. The paper documents the empirical regularities in the term structure of credit spreads and spread volatilities that make this result possible. In addition, we present empirical evidence of the imprint made on corporate bond returns by the widespread use of the asset swaps data.     

A jump to default extended CEV model: an application of Bessel processes

We develop a flexible and analytically tractable framework which unifies the valuation of corporate liabilities, credit derivatives, and equity derivatives. We assume that the stock price follows a diffusion, punctuated by a possible jump to zero (default). To capture the positive link between default and equity volatility, we assume that the hazard rate of default is an increasing affine function of the instantaneous variance of returns on the underlying stock. To capture the negative link between volatility and stock price, we assume a constant elasticity of variance (CEV) specification for the instantaneous stock volatility prior to default. We show that deterministic changes of time and scale reduce our stock price process to a standard Bessel process with killing. This reduction permits the development of completely explicit closed form solutions for risk-neutral survival probabilities, CDS spreads, corporate bond values, and European-style equity options. Furthermore, our valuation model is sufficiently flexible so that it can be calibrated to exactly match arbitrarily given term structures of CDS spreads, interest rates, dividend yields, and at-the-money implied volatilities.     

What Drives the Cross‐Section of Credit Spreads?: A Variance Decomposition Approach

I decompose the variation of credit spreads for corporate bonds into changing expected returns and changing expectation of credit losses. Using a log‐linearized pricing identity and a vector autoregression applied to microlevel data from 1973 to 2011, I find that expected returns contribute to the cross‐sectional variance of credit spreads nearly as much as expected credit loss does. However, most of the time‐series variation in credit spreads for the market portfolio corresponds to risk premiums.     

The information content of option-implied volatility for credit default swap valuation

Credit default swaps (CDS) are similar to out-of-the-money put options in that both offer a low cost and effective protection against downside risk. This study investigates whether put option-implied volatility is an important determinant of CDS spreads. Using a large sample of firms with both CDS and options data, we find that individual firms’ put option-implied volatility dominates historical volatility in explaining the time-series variation in CDS spreads. To understand this result, we show that implied volatility is a more efficient forecast for future realized volatility than historical volatility. More importantly, the volatility risk premium embedded in option prices covaries with the CDS spread. These findings complement existing empirical evidence based on market-level data.     

Market conditions,default risk and credit spreads

This study empirically examines the impact of the interaction between market and default risk on corporate credit spreads. Using credit default swap (CDS) spreads, we find that average credit spreads decrease in GDP growth rate, but increase in GDP growth volatility and jump risk in the equity market. At the market level, investor sentiment is the most important determinant of credit spreads. At the firm level, credit spreads generally rise with cash flow volatility and beta, with the effect of cash flow beta varying with market conditions. We identify implied volatility as the most significant determinant of default risk among firm-level characteristics. Overall, a major portion of individual credit spreads is accounted for by firm-level determinants of default risk, while macroeconomic variables are directly responsible for a lesser portion.     

A Multifactor Model of Credit Spreads

We represent credit spreads across ratings as a function of common unobservable factors of the Vasicek form. Using a state-space approach we estimate the factors, their process parameters, and the exposure of each observed credit spread series to each factor. We find that most of the systematic variation across credit spreads is captured by three factors. The factors are closely related to the implied volatility index (VIX), the long bond rate, and S&P500 returns, supporting the predictions of structural models of default at an aggregate level. By making no prior assumption about the determinants of yield spread dynamics, our study provides an original and independent test of theory. The results also contribute to the current debate about the role of liquidity in corporate yield spreads. While recent empirical literature shows that the level and time-variation in corporate yield spreads is driven primarily by a systematic liquidity risk factor, we find that the three most important drivers of yield spread levels relate to macroeconomic variables. This suggests that if credit spread levels do contain a large liquidity premium, the time variation of this premium is likely driven by the same factors as default risk.     

THE CHALLENGE OF MANAGING CREDIT SPREADS: NEW TOOLS ON THE HORIZON

Broadly speaking, "credit spread" is the excess of a bond's yield over the appropriate Treasury rate that is necessary to compensate lenders for the perceived risk of default. In the primary market, credit spreads can significantly increase a corporation's borrowing cost. In the secondary market, the effect of fluctuating spreads is of material concern to investors and traders.
Credit spreads have exhibited considerable volatility in recent years, particularly during the fall of 1998. Although the absolute levels of spreads have ranged widely in recent years, from a few basis points to several hundred depending on rating category, their volatilities have been remarkably similar—about 25 to 39% on an annual basis. In spite of this high volatility, there are currently no cost-effective means of hedging credit spreads.
The recently announced S&P Credit Indices presage a leap forward in credit derivatives. Computed from the prices of selected liquid bonds, these indices can serve as a basis for exchange-traded futures contracts and options—tools that are in great demand for managing credit spread exposure.     

A jump-diffusion model for pricing corporate debt securities in a complex capital structure

This paper proposes a jump-diffusion model, in closed form, to price corporate debt securities, senior and junior, with the same maturity and violation of the absolute priority rule. We take the structural approach that the firm's asset value follows a jump-diffusion process in a stochastic interest rate economy. Default occurs only if the firm value at the maturity of the corporate debts is less than the sum of the prespecified face values. Unlike previous models in the structural approach, our model is consistent with the current term structures of credit spreads for both senior and junior debts. In particular, it captures realistic short maturity credit spreads observed in the market. The key idea is to allow the jump intensity to be a time-dependent function. As an application, valuation of credit spread options is presented.     

Value investing in credit markets

We outline a parsimonious empirical model to assess the relative usefulness of accounting- and equity market-based information to explain corporate credit spreads. The primary determinant of corporate credit spreads is the physical default probability. We compare existing accounting-based and market-based models to forecast default. We then assess whether the credit market completely incorporates this default information into credit spreads. We find that credit spreads reflect information about forecasted default rates with a significant lag. This unique evidence suggests a role for value investing in credit markets.     

Do significant risk warnings in annual reports increase corporate bond credit spreads? Evidence from China

Based on listed companies issuing bonds on the Shanghai and Shenzhen Stock Exchanges from 2007 to 2017, this study analyzes the relationship between significant risk warnings in Chinese companies’ annual reports and corporate bond credit spreads. The main findings are as follows. First, in the Chinese market, “substantial warnings of significant risks” can significantly improve corporate bond credit spreads, reflecting the risk-warning effect; second, state-owned property rights weaken this effect, which only pertains to listed companies with poor risk management and low information quality; third, significant risk warnings increase investors’ heterogeneous beliefs, also affecting credit spreads; and fourth, through textual analysis, it is found that the corporate bond credit spread is greater when the disclosed risk factors are more pessimistic and less similar to those of the previous year. The findings of this paper help to enrich the literature on credit spreads and risk disclosure.     

The term structure of credit spreads with jump risk

Default risk analysis is important for valuing corporate bonds, swaps, and credit derivatives and plays a critical role in managing the credit risk of bank loan portfolios. This paper offers a theory to explain the observed empirical regularities on default probabilities, recovery rates, and credit spreads. It incorporates jump risk into the default process. With the jump risk, a firm can default instantaneously because of a sudden drop in its value. As a result, a credit model with the jump risk is able to match the size of credit spreads on corporate bonds and can generate various shapes of yield spread curves and marginal default rate curves, including upward-sloping, downward-sloping, flat, and hump-shaped, even if the firm is currently in a good financial standing. The model also links recovery rates to the firm value at default so that the variation in recovery rates is endogenously generated and the correlation between recovery rates and credit ratings before default reported in Altman [J. Finance 44 (1989) 909] can be justified.     

Liquidity or Credit Risk? The Determinants of Very Short‐Term Corporate Yield Spreads

Employing a comprehensive database on transactions of commercial paper issued by domestic U.S. nonfinancial corporations, we study the determinants of very short‐term corporate yield spreads. We find that liquidity plays a role in the determination of spreads but, somewhat surprisingly, credit quality is the more important determinant of spreads, even at horizons of less than 1 month. These results are robust across a variety of proxies for liquidity and credit risk, and have important implications for the literature on the modeling of corporate bond prices.     

Credit derivatives and bank credit supply

Credit derivatives are the latest in a series of innovations that have had a significant impact on credit markets. Using a micro data set of individual corporate loans, this paper explores whether use of credit derivatives is associated with an increase in bank credit supply. We find only limited evidence that greater use of credit derivatives is associated with greater supply of bank credit. The strongest effect is for large term loans—newly negotiated loan extensions to large corporate borrowers, with a largely negative impact on (previously negotiated) commitment lending. Even for large term borrowers, increases in the volume of credit are offset by higher spreads. These findings suggest that the benefits of the growth of credit derivatives may be narrow, accruing mainly to large firms that are likely to be “named credits” in these transactions. Finally, use of credit derivatives appears to be complementary to other forms of hedging by banks, though the banks most active in hedging appear to charge more for additional amounts of credit.     

Institutional ownership and credit spreads: An information asymmetry perspective

Recent literature has documented a link between institutional equity ownership (IO) and cost of debt capital, and interpreted it as a corporate governance effect. However, institutional equity investors may also affect cost of debt through their influence on information asymmetry condition of firms. To distinguish between the two effects, we break down institutional investors into different groups: transient institutional investors (TRA who are sensitive to information asymmetry but unlikely to participate in corporate governance, and the dedicated ones (DED) who act oppositely. Based on a most up-to-date and comprehensive bond data spanning the past 20 years, we find that credit spreads narrow (widen) with an increase in equity ownership by TRA (DED). The effects are most prominent among short-term bonds, bonds with lower ratings, higher leverage and higher volatilities. The results persist after controlling for potential endogeneity and other information asymmetry measures, and are unlikely due to an asset substitution effect. Overall, our findings provide strong support for the effect of information asymmetry on credit spread, and highlight the importance of distinguishing various types of institutional investors.     

The Determinants of Implied Volatility: A Test Using LIFFE Option Prices

This paper presents and tests a model of the volatility of individual companies' stocks, using implied volatilities derived from option prices. The data comes from traded options quoted on the London International Financial Futures Exchange. The model relates equity volatilities to corporate earnings announcements, interest-rate volatility and to four determining variables representing leverage, the degree of fixed-rate debt, asset duration and cash flow inflation indexation. The model predicts that equity volatility is positively related to duration and leverage and negatively related to the degree of inflation indexation and the proportion of fixed-rate debt in the capital structure. Empirical results suggest that duration, the proportion of fixed-rate debt, and leverage are significantly related to implied volatility. Regressions using all four determining variables explain approximately 30% of the cross-sectional variation in volatility. Time series tests confirm an expected drop in volatility shortly after the earnings announcement and in most cases a positive relationship between the volatility of the stock and the volatility of interest rates.     

Changes in volatility of credit spread and market efficiency during rapid growth of credit-related securities

This paper investigates the changes in credit spread volatility during 1993–2001. We find that the credit spreads between junk-grade corporate bonds and Treasury bonds were significantly more volatile in the second half of this period when credit-related securities became popular. In contrast, investment-grade bonds exhibited no significant change in volatility. The junk bonds variance ratios changed from being less than one to greater than one. Using the GJR-Garch model, the conditional volatilities of junk bonds increased in the second half of the period and the mean reversion speeds slowed, suggesting a longer time for mean reversion to occur. Our analysis rules out treasury volatility, credit spread level, equity market return, T-bill rate, curvature of the Treasury curve, financial crisis, quantity of defaults and standard deviation of defaults as explanations for the increase in junk bond volatility. In contrast, volatility of equity returns provides a partial explanation of junk bond spread volatility in the later period.     

信息不对称与企业债券信用风险估价——基于2008-2011年中国企业债券数据

企业债券发行者与投资者之间的信息不对称,导致投资者因为承担更大的风险而要求更高的回报,从而推高企业债券发行者的信用利差。本文利用2008年12月31日至2011年12月31日中国非上市企业债券数据,实证检验企业债券发行者和投资者之间的信息不对称对企业债券信用利差的影响。我们发现,企业债券发行者和投资者之间的信息不对称程度与企业债券信用利差之间存在显著的正相关性。同时,本文构建了包含信息不对称的企业债券信用风险估价模型。与传统的BS估价模型相比,本文构建的估价模型能更好地拟合实际信用利差。     

A time-varying common risk factor affecting corporate yield spreads

A time-varying common risk factor affecting corporate yield spreads is modelled by extending a panel data model. The panel data model accommodates a common factor, which is associated with time-varying individual effects. The factor multiplied by a bond-specific unobservable is identified as a systematic risk premium. In disentangling the systematic risk premium, both credit and liquidity risks are evaluated; the credit risk is assessed by bond rating, and the liquidity risk is indirectly measured by discrepancy in quoted yields by brokerage firms. Parameters are estimated by the generalized method of moments procedure. The model is tested on the corporate bond market in Japan. Empirical results show that the time-varying common risk factor is successfully estimated together with credit and liquidity risks.     

Corporate bond default risk: A 150-year perspective

We study corporate bond default rates using an extensive new data set spanning the 1866-2008 period. We find that the corporate bond market has repeatedly suffered clustered default events much worse than those experienced during the Great Depression. For example, during the railroad crisis of 1873-1875, total defaults amounted to 36% of the par value of the entire corporate bond market. Using a regime-switching model, we examine the extent to which default rates can be forecast by financial and macroeconomic variables. We find that stock returns, stock return volatility, and changes in GDP are strong predictors of default rates. Surprisingly, however, credit spreads are not. Over the long term, credit spreads are roughly twice as large as default losses, resulting in an average credit risk premium of about 80 basis points. We also find that credit spreads do not adjust in response to realized default rates.