News and the cross-section of expected corporate bond returns

We study the cross-section of expected corporate bond returns using an inter-temporal CAPM (ICAPM) with three-factors: innovations in future excess bond returns, future real interest rates and future expected inflation. Our test assets are a broad range of corporate bond market index portfolios. We find that two factors – innovations about future inflation and innovations about future real interest rates – explain the cross-section of expected corporate bond returns in our sample. Our model provides an alternative to the ad hoc risk factor models used, for example, in evaluating the performance of bond mutual funds.     

Corporate Bond Returns and Volatility

Recent literature emphasizes the relation of stock volatility to corporate bond yields. We demonstrate that during 1996–2005 corporate bond excess return volatility is directly related to contemporaneous corporate bond excess returns. In fact, the decompositions of aggregate bond volatility have a higher contemporaneous correlation with bond yields in comparison to idiosyncratic stock risk. Additionally, bond volatility and idiosyncratic risk are significant predictors of corporate three‐month and six‐month ahead bond excess returns. We also find that corporate bond volatility contains both slow moving and time‐varying components.     

Common risk factors in the cross-section of corporate bond returns

We investigate the cross-sectional determinants of corporate bond returns and find that downside risk is the strongest predictor of future bond returns. We also introduce common risk factors based on the prevalent risk characteristics of corporate bonds—downside risk, credit risk, and liquidity risk—and find that these novel bond factors have economically and statistically significant risk premiums that cannot be explained by long-established stock and bond market factors. We show that the newly proposed risk factors outperform all other models considered in the literature in explaining the returns of the industry- and size/maturity-sorted portfolios of corporate bonds.     

Detecting time-variation in corporate bond index returns: A smooth transition regression model

This paper investigates the time-varying corporate bond index returns in a multi-factor smooth transition regression model. We find that expected index returns vary between weak and strong economic regimes, where the transition from one regime to the other is governed by the 3-quartered growth of industrial production. Weak economic regimes are characterized by low growth of industrial production, vice versa for strong economic regimes. Further, risk factor sensitivities are generally more negative in strong economic regimes than in weak regimes, implying that index returns are low when economic conditions are good and high when economic conditions are poor.     

Aggregate Earnings and Corporate Bond Markets

I examine the previously unexplored relation between aggregate earnings changes and corporate bond market returns. I find that aggregate earnings changes have a negative relation to investment‐grade corporate bond market returns and a positive relation to high‐yield corporate bond market returns. The aggregate earnings‐returns relation is lower (i.e., less positive or more negative) for bonds with higher credit ratings and longer maturities. Further, I show that the aggregate earnings‐returns relation is driven by both the expected and the news component of aggregate earnings changes. The expected component is negatively related to expected returns, and the news component is positively related to cash flow news and changes in nominal interest rates, and negatively related to changes in default premia. My results contribute to the understanding of the role of earnings in corporate bond markets as well as the macroeconomic role of accounting information.     

On the suboptimality of single-factor exercise strategies for Bermudan swaptions

This paper examines the interaction between momentum in the returns of equities and corporate bonds. We find that investment grade corporate bonds do not exhibit momentum at the three- to 12-month horizons. Instead, the evidence suggests that they exhibit reversals. However, significant evidence exists of a momentum spillover from equities to investment grade corporate bonds of the same firm. Firms earning high (low) equity returns over the previous year earn high (low) bond returns the following year. The spillover results are stronger among firms with lower-grade debt and higher equity trading volume, seem robust to various risk and liquidity controls, and hold even after controlling for past earnings surprises. In examining the source of the spillover, we find that the bond ratings of firms with positive (negative) equity momentum continue to improve (deteriorate) in the future, suggesting underreaction to the information in past equity prices about changing default risk is a likely source of the spillover effect. Overall, our results suggest that both equity and debt underreact to firm fundamentals, but past equity returns is a better proxy of firm fundamentals than past bond returns.     

Stock and bond returns with Moody Investors

We present a tractable, linear model for the simultaneous pricing of stock and bond returns that incorporates stochastic risk aversion. In this model, analytic solutions for endogenous stock and bond prices and returns are readily calculated. After estimating the parameters of the model by the general method of moments, we investigate a series of classic puzzles of the empirical asset pricing literature. In particular, our model is shown to jointly accommodate the mean and volatility of equity and long term bond risk premia as well as salient features of the nominal short rate, the dividend yield, and the term spread. Also, the model matches the evidence for predictability of excess stock and bond returns. However, the stock–bond return correlation implied by the model is somewhat higher than that in the data.     

Structural models of credit risk are useful: Evidence from hedge ratios on corporate bonds

Structural models of credit risk provide poor predictions of bond prices. We show that, despite this, they provide quite accurate predictions of the sensitivity of corporate bond returns to changes in the value of equity (hedge ratios). This is important since it suggests that the poor performance of structural models may have more to do with the influence of non-credit factors rather than their failure to capture the credit exposure of corporate debt. The main result of this paper is that even the simplest of the structural models [Merton, R., 1974. On the pricing of corporate debt: the risk structure of interest rates. Journal of Finance 29, 449–470] produces hedge ratios that are not rejected in time-series tests. However, we find that the Merton model (with or without stochastic interest rates) does not capture the interest rate sensitivity of corporate debt, which is substantially lower than would be expected from conventional duration measures. The paper also shows that corporate bond prices are related to a number of market-wide factors such as the Fama-French SMB (small minus big) factor in a way that is not predicted by structural models.     

Pricing New Corporate Bond Issues: An Analysis of Issue Cost and Seasoning Effects

The pricing of new corporate bond issues is examined, with particular emphasis on the seasoning effect and the cost of underwriting. Considerable attention is paid to some special features of the corporate bond market, including the use of actual trader quotes so as to accurately measure holding period returns. Our results suggest that the cost of issuing corporate bonds is less than previously reported.     

Liquidity risk of corporate bond returns: conditional approach

We study the exposure of the US corporate bond returns to liquidity shocks of stocks and Treasury bonds over the period 1973–2007 in a regime-switching model. In one regime, liquidity shocks have mostly insignificant effects on bond prices, whereas in another regime, a rise in illiquidity produces significant but conflicting effects: Prices of investment-grade bonds rise while prices of speculative-grade (junk) bonds fall substantially (relative to the market). Relating the probability of these regimes to macroeconomic conditions we find that the second regime can be predicted by economic conditions that are characterized as “stress.” These effects, which are robust to controlling for other systematic risks (term and default), suggest the existence of time-varying liquidity risk of corporate bond returns conditional on episodes of flight to liquidity. Our model can predict the out-of-sample bond returns for the stress years 2008–2009. We find a similar pattern for stocks classified by high or low book-to-market ratio, where again, liquidity shocks play a special role in periods characterized by adverse economic conditions.     

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.     

Return reversals in the bond market: Evidence and causes

The finance literature has shown that equity returns are predictable using past returns. This study extends that literature by examining bond return predictability. Using returns constructed from dealer bid prices, we find short- to intermediate-term reversals in investment grade corporate bond returns. These reversals are larger in the first half of the sample period and consistent with the predictions of dealer inventory cost models. This supports Jegadeesh and Titman’s [J. Financ. Intermed. 4 (1995) 116] assertion that daily, weekly, and monthly reversals in equity returns come from dealer inventory considerations, not behavioral biases. Finally, unlike equity returns, we find no evidence of momentum in bond returns.     

Corporate bond credit spreads and forecast dispersion

Recent research establishes a negative relation between stock returns and dispersion of analysts’ earnings forecasts, arguing that asset prices more reflect the views of optimistic investors because of short-sale constraints in equity markets. In this article, we examine whether a similar effect prevails in corporate bond markets. After controlling for common bond-level, firm-level, and macroeconomic variables, we find evidence that bonds of firms with higher dispersion demand significantly higher credit spreads than otherwise similar bonds and that changes in dispersion reliably predict changes in credit spreads. This evidence suggests a limited role of short-sale constraints in our corporate bond data sets. Consistent with a rational explanation, dispersion appears to proxy largely for future cash flow uncertainty in corporate bond markets.     

Are corporate bond market returns predictable?

This paper examines the predictability of corporate bond returns using the transaction-based index data for the period from October 1, 2002 to December 31, 2010. We find evidence of significant serial and cross-serial dependence in daily investment-grade and high-yield bond returns. The serial dependence exhibits a complex nonlinear structure. Both investment-grade and high-yield bond returns can be predicted by past stock market returns in-sample and out-of-sample, and the predictive relation is much stronger between stocks and high-yield bonds. By contrast, there is little evidence that stock returns can be predicted by past bond returns. These findings are robust to various model specifications and test methods, and provide important implications for modeling the term structure of defaultable bonds.     

Oil prices implied volatility or direction: Which matters more to financial markets?

We examine the impact of oil price uncertainty on US stock returns by industry using the US Oil Fund options implied volatility OVX index and a GJR-GARCH model. We test the effect of the implied volatility of oil on a wide array of domestic industries’ returns using daily data from 2007 to 2016, controlling for a variety of variables such as aggregate market returns, market volatility, exchange rates, interest rates, and inflation expectations. Our main finding is that the implied volatility of oil prices has a consistent and statistically significant negative impact on nine out of the ten industries defined in the Fama and French (J Financ Econ 43:153–193, 1997) 10-industry classification. Oil prices, on the other hand, yield mixed results, with only three industries showing a positive and significant effect, and two industries exhibiting a negative and significant effect. These findings are an indication that the volatility of oil has now surpassed oil prices themselves in terms of influence on financial markets. Furthermore, we show that both oil prices and their volatility have a positive and significant effect on corporate bond credit spreads. Overall, our results indicate that oil price uncertainty increases the risk of future cash flows for goods and services, resulting in negative stock market returns and higher corporate bond credit spreads.     

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.     

Liquidity risk and expected corporate bond returns

This paper studies the pricing of liquidity risk in the cross section of corporate bonds for the period from January 1994 to March 2009. The average return on bonds with high sensitivities to aggregate liquidity exceeds that for bonds with low sensitivities by about 4% annually. The positive relation between expected corporate bond returns and liquidity beta is robust to the effects of default and term betas, liquidity level, and other bond characteristics, as well as to different model specifications, test methodologies, and a variety of liquidity measures. The results suggest that liquidity risk is an important determinant of expected corporate bond returns.     

Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets

To identify disruptions in credit markets, research on the role of asset prices in economic fluctuations has focused on the information content of various corporate credit spreads. We re-examine this evidence using a broad array of credit spreads constructed directly from the secondary bond prices on outstanding senior unsecured debt issued by a large panel of nonfinancial firms. An advantage of our “ground-up” approach is that we are able to construct matched portfolios of equity returns, which allows us to examine the information content of bond spreads that is orthogonal to the information contained in stock prices of the same set of firms, as well as in macroeconomic variables measuring economic activity, inflation, interest rates, and other financial indicators. Our portfolio-based bond spreads contain substantial predictive power for economic activity and outperform—especially at longer horizons—standard default-risk indicators. Much of the predictive power of bond spreads for economic activity is embedded in securities issued by intermediate-risk rather than high-risk firms. According to impulse responses from a structural factor-augmented vector autoregression, unexpected increases in bond spreads cause large and persistent contractions in economic activity. Indeed, shocks emanating from the corporate bond market account for more than 30 percent of the forecast error variance in economic activity at the two- to four-year horizon. Overall, our results imply that credit market shocks have contributed significantly to US economic fluctuations during the 1990-2008 period.     

The effects of tax policy on financial markets: G3 evidence

We investigate the effects of various tax policy innovations on stock market returns. By using a vector autoregressive model that controls for the mutual causality between fiscal policy and financial market performance, we test whether financial markets serve as a transmission mechanism for tax policy innovations. Our findings indicate that indirect taxes have a larger effect on market returns than do labor taxes. Further, corporate tax innovations do not have any statistically significant effect on stock returns. We consider that this finding is a result of a firm's ability to switch between equity financing and bond financing.     

Why are excess returns on China’s Treasury bonds so predictable? The role of the monetary system

It is well documented that the time-varying bond excess returns can be explained by predetermined variables such as information in the term structure and macro economic variables. Recent studies suggest that demand and supply of bonds influence bond excess returns. We extend the literature and find that monetary system attributes affect return dynamics in the bond market. By introducing a theoretical model to forecast excess returns on Treasury bonds in the context of China’s unique monetary system, this paper attributes the predicted components of bond excess returns mainly to the inflexible term structures of official interest rates set by China’s central bank.