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Empirical Duration of Corporate Bonds and Credit Market Segmentation
... These stochastic metrics are then immune to the issues that bond yields for different maturities can change by different magnitudes following changes in the spot rate, for notable practitioner studies on bond duration and convexity. See, for example, Ben Dor et al. (2007); Ambastha et al. (2010); and Hyman et al. (2014). Much of this literature derives the duration from firm-value based structural models à la Merton (1974) or estimates the empirical relation between changes in the prices of corporate bonds and changes in interest rates. ...
... Similarly, the defensive corporate factor buys AAA-rated corporate bonds with a maturity between one and three years and shorts a duration equivalent position of BBB-rated bonds with maturities between seven and ten years. Strong evidence exists that investment-grade and high-yield markets are treated as two separate asset classes by market participants and that this segmentation ultimately affects bond prices (Ambastha et al., 2010;Z. Chen et al., 2014). ...
... These stochastic metrics are then immune to the issues that bond yields for different maturities can change by different magnitudes following changes in the spot rate, for notable practitioner studies on bond duration and convexity. See, for example, Ben Dor et al. (2007); Ambastha et al. (2010); and Hyman et al. (2014). Much of this literature derives the duration from firm-value based structural models à la Merton (1974) or estimates the empirical relation between changes in the prices of corporate bonds and changes in interest rates. ...
... In the case of stochastic duration and convexity, the price change is reflected in the spot rate instead of the yield to maturity. These stochastic metrics are then immune to the issues that bond yields for different maturities can change by different magnitudes following changes in the spot rate, for notable practitioner studies on bond duration and convexity, see Ben Dor et al. (2007), Ambastha et al. (2010), Hyman et al. (2014). Much of this literature derives the duration from firm-value based structural models à la Merton (1974) or estimate the empirical relation between changes in the prices of corporate bonds and changes in interest rates, see Leland (1994); Nawalkha (1996); Jacoby and Roberts (2003); Sarkar and Hong (2004). ...
We investigate the impact of credit spreads on the stochastic duration and convexity of corporate bonds with respect to the very metrics for equivalent Treasury bonds. We show that the credit spread has two interacting effects on both the duration and the convexity of a corporate coupon bond as compared to those of an equivalent Treasury coupon bond. For bond convexity, we newly uncover that the first impact originates from the duration of the Treasuries and from both duration and convexity of the coupon bonds conditional survival probability, and the covariance between the default-free short rate and the credit spread. The second driving factor stems from the weighting of the convexities of the zero-coupon bonds. We provide necessary and sufficient conditions for the duration and convexity of defaultable corporate coupon bonds to be smaller than those of equivalent Treasury bonds. The numerical experiments confirm our theoretical results. Given the empirical evidence that interest rates and credit spreads are, by and large, negatively correlated, our results support the notion that not only durations but also convexities of defaultable corporate bonds may be smaller than those of equivalent Treasuries.
... Ratings affect the cost of capital (e.g., Kisgen and Strahan, 2010;Baghai et al., 2014) and important corporate financial decisions, such as those relating to capital structure (e.g., Kisgen, 2006;Bedendo and Siming, 2018) and debt policy (Graham and Harvey, 2001). Rating changes may also affect firm's financial and investment policy, beyond changes in fundamentals (e.g., Nini et al., 2012;Manso, 2013;Graham et al., 2015;Almeida et al., 2017;Bedendo and Siming, 2018) and may lead to market segmentation into SG and IG investor clientele (e.g., Ambastha et al., 2010;Chen et al., 2014). ...
Institutional portfolio managers face rating-based constraints established by investment policy statements (IPS) in their delegated management which, similar to formal regulations, restrict investment in bonds below certain rating levels. We analyze how prices and liquidity of corporate bonds traded by institutional investors react to downgrades crossing these internal limits. Consistent with the existence of a non-regulatory transmission channel, downgrades through regular IPS ratings have a greater impact on the market than downgrades crossing regulatory boundaries, such as the investment-speculative frontier and the NAIC's risk-based capital system thresholds. Our results reveal the existence of other source of rating-related frictions that should be considered.
... To do so, we use the overlapping portfolio approach of Jegadeesh and Titman (1993) . We split the corporate bond universe into investment grade and high yield as they are effectively seen as two different asset classes by practitioners and academics (e.g., Ambastha et al., 2010 ). ...
High (low) quality stocks generate anomalously high (low) returns from the stand point of prominent asset pricing models. We provide a comprehensive overview of the commonly used quality definitions, and test their predictive power for stock returns. We show that quality measures predict stock returns if and only if they forecast earnings growth, and that this information is not contained in other characteristics that have been shown to drive expected returns on stocks. Our results provide empirical evidence supporting the theoretical relation between profitability, investments, and expected stock returns, proposed by Fama and French (2015), across various markets, and thereby help better understand the existence of the quality anomaly.
... We created the factor portfolios separately for investment grade and high yield because these market segments are treated as two separate asset classes by financial market participants, including (1) asset owners (making separate allocations to investment grade and high yield), (2) both passive and active asset managers (offering separate investment products for investment grade and high yield), (3) index providers (offering separate indexes for investment grade and high yield), and (4) regulators (often prohibiting certain groups of institutional investors from holding high-yield bonds). Evidence on the division of the corporate bond market into investment-grade and high-yield segments is provided by Ambastha, Ben Dor, Dynkin, Hyman, and Konstantinovsky (2010) and Chen, Lookman, Schürhoff, and Seppi (2014). Chen et al. (2014) mentioned that a large stream of extant theoretical literature shows that labels (in this case, ratings of corporate bonds) can lead to market segmentation and asset class effects by influencing investors' willingness to hold the security and can thus affect security prices. ...
We offer empirical evidence that size, low-risk, value, and momentum factor portfolios generate economically meaningful and statistically significant alphas in the corporate bond market. Because the correlations between the single-factor portfolios are low, a combined multifactor portfolio benefits from diversification among the factors: It has a lower tracking error and a higher information ratio than the individual factors. Our results are robust to transaction costs, alternative factor definitions, alternative portfolio construction settings, and constructing factor portfolios on a subsample of liquid bonds. Finally, allocating to corporate bond factors provides added value beyond allocating to equity factors in a multi-asset context.
... We created the factor portfolios separately for investment grade and high yield because these market segments are treated as two separate asset classes by financial market participants, including (1) asset owners (making separate allocations to investment grade and high yield), (2) both passive and active asset managers (offering separate investment products for investment grade and high yield), (3) index providers (offering separate indexes for investment grade and high yield), and (4) regulators (often prohibiting certain groups of institutional investors from holding high-yield bonds). Evidence on the division of the corporate bond market into investment-grade and high-yield segments is provided by Ambastha, Ben Dor, Dynkin, Hyman, and Konstantinovsky (2010) and Chen, Lookman, Schürhoff, and Seppi (2014). Chen et al. (2014) mentioned that a large stream of extant theoretical literature shows that labels (in this case, ratings of corporate bonds) can lead to market segmentation and asset class effects by influencing investors' willingness to hold the security and can thus affect security prices. ...
We provide empirical evidence that Size, Low-Risk, Value and Momentum factor portfolios generate economically meaningful and statistically significant alphas in the corporate bond market. As the correlations between the single-factor portfolios are low, a combined multi-factor portfolio benefits from diversification between the factors: it has a lower tracking error and a higher information ratio than the individual factors. The results are robust to transaction costs, alternative factor definitions, alternative portfolio construction settings and the evaluation on a subsample of liquid bonds. Finally, allocating to corporate bond factors has added value beyond allocating to equity factors in a multi-asset context.
... Similar results are found in the hedging literature. For example, early work by Grieves (1985) and Landes, Stoffels and Seifert (1985), and recent work by Ambastha et al. (2010) showed that the hedge ratio of corporate bonds to Treasury bonds decreases as the credit quality goes down. Furthermore, Grieves (1985) showed that lower graded corporate bonds have larger hedge ratios to equity futures contracts, which can be seen as a proxy for credit risk. ...
Corporate bond returns consist of two distinct components: an interest rate component, which is default-free and anti-cyclical, and a credit spread component, which is default-risky and pro-cyclical. These components are mutually negatively correlated and their relative importance varies with credit quality. We show that it is of critical importance to take this into account when studying the predictability of corporate bond returns. In this paper we focus on the credit spread component of corporate bond returns, enabling us to find new predictors that were previously unknown to the literature. Moreover, by re-examining previously documented predictors, we are able to dismiss several of them as irrelevant for credit spread returns and to explain inconsistent findings between investment grade and high yield corporate bonds. In total, we find four factors with significant in-sample and out-of-sample predictability of both investment grade and high yield excess returns over Treasury. Two variables come from the existing literature: past equity return and past corporate bond return. Evidence for the other two variables is new: change in implied equity volatility and the Halloween indicator.
We examine the role of five liquidity dimensions in the U.S. corporate bond market from a broad set of liquidity proxies. Based on the observed different level of liquidity shown by investment-grade and high-yield bonds, we analyze the ability of several liquidity proxies and dimensions to classify bonds within these two main credit rating classes. Our findings show that, at the individual level, all tightness-based liquidity metrics outperform the other measures. In this way, a few proxies measuring transaction costs can help detect high-yield bonds whose liquidity behaves similarly to that of investment-grade bonds. At the dimension level, we find that the theoretical separation into liquidity dimensions is empirically corroborated, but the joint consideration of all of them only brings a slight improvement in results.
The aim of this paper is to highlight the dynamic relationship between interest rates and credit spreads, analyzing
the impact on portfolio management in terms of duration. Understanding this relationship provides key
information to improve the tactical asset allocation decision between risk-free bonds and bonds with credit risk.
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