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.

Using detailed loan holding data of Collateralized Loan Obligations (CLOs), we document empirical evidence for the fire sale of leveraged loans due to leverage constraints on CLOs. Constrained CLOs are forced to sell loans downgraded to CCC or below, and thus loans widely held by constrained CLOs experience temporary price depreciation. This instability is exacerbated by diversification requirements. As the CLO market grows, each CLO’s effort to diversify its portfolio leads to similarity in loan holdings among CLOs, and thus their leverage constraints simultaneously bind. CLOs’ overlapping loan holdings spread idiosyncratic shocks to large borrowers to the overall leveraged loan market.

We study a search and bargaining model of over‐the‐counter markets for nonstandardized assets of heterogeneous quality. Once matched, investors privately learn their values positively correlated with asset quality. Bargaining results in delay that is hump‐shaped in quality and U‐shaped in asset turnover. We document these patterns in commercial real estate and corporate bonds markets. Extreme qualities are little affected by changes in asset standardization, while intermediate qualities are more susceptible. For nonstandardized assets, opacity ensures active trading of all assets, which explains why their trading is decentralized and suggests that trade centralization should come with greater standardization.

We decompose corporate bond and equity index returns into duration-matched government bond returns and the excess returns over this duration-matched counterfactual, which we term duration-adjusted returns. Compared with previously used excess return definitions (ie, returns in excess of Treasury bills), our decomposition leads to markedly different return patterns and asset pricing implications. In particular, we find that investment-grade bonds earn a small credit risk premium, comparable in magnitude to the convenience yield, and that duration adjustment resolves the CAPM’s failure to price corporate bonds. These findings highlight the importance of adjusting for nonstationary interest rate environments in asset pricing tests.

We present a novel empirical benchmark for analyzing credit risk using “pseudo firms” that purchase traded assets financed with equity and zero-coupon bonds. By no-arbitrage, pseudo bonds are equivalent to Treasuries minus put options on pseudo firm assets. Empirically, like corporate spreads, pseudo bond spreads are large, countercyclical, and predict lower economic growth. Using this framework, we find that bond market illiquidity, investors' overestimation of default risks, and corporate frictions do not seem to explain excessive observed credit spreads but, instead, a risk premium for tail and idiosyncratic asset risks is the primary determinant of corporate spreads.

Using transaction data from the first half of 2020, we examine the reaction of corporate credit spreads to the Federal Reserve’s monetary policy announcements. We find evidence that the bond markets are segmented across credit ratings, which led to different initial reactions across bonds with different credit ratings but spread across various sectors of corporate bonds over the longer event window. To quantify the default risk channel of quantitative easing, we apply the variance decomposition approach to credit spreads and find that a significant fraction of credit spread changes indeed correspond to reduced default risk caused by the corporate bond purchase program. In contrast, we only find mixed evidence for the liquidity channel driving the market reaction.

This paper examines dealer inventory capacity, or liquidity supply, as a driver of liquidity and expected returns in the corporate bond market. We identify shocks to aggregate liquidity supply using data on corporate bond yields and dealer positions. Liquidity supply shocks lead to persistent changes in market liquidity, are correlated with proxies for dealer financial constraints, and have significant explanatory power for cross‐sectional and time‐series variation in expected returns, beyond standard risk factors. Our findings point to liquidity supply by financially constrained intermediaries as a main driver of market liquidity and asset prices.

This study examines how options trading influences brand development strategies by encouraging managerial risk-taking. We find that firms with higher levels of options trading tend to introduce more new trademarks, which exhibit lower citation rates from subsequent trademarks. These firms favor brand creation over extension, leading to increased brand riskiness, as evidenced by greater trademark diversity. Potential channels for these effects include increased institutional ownership by transient investors and enhanced managerial hedging opportunities. These effects are more pronounced in firms with weaker governance, managers with higher pay-risk sensitivity, younger managerial teams, and intense competition. Additionally, we observe a negative relation between unrelated brand diversification, driven by options trading, and firm value. Our findings support the notion that active options markets incentivize managers to pursue riskier brand strategies.

Corporate bonds’ book-to-market ratios predict returns computed from transaction prices. Senior bonds (even investment grade) with the 20% highest ratios outperform the 20% lowest by 3%–4% annually after non-parametrically controlling for numerous liquidity, default, microstructure, and priced-risk attributes: yield-to-maturity, bid–ask spread, duration/maturity, credit spread/rating, past returns, coupon, size, age, industry, and structural model equity hedges. Spreads for all-bond samples are larger. An efficient bond market would not exhibit the observed decay in the ratio’s predictive efficacy with implementation delays, small yield-to-maturity spreads, or similar-sized spreads across bonds with differing risks. A methodological innovation avoids liquidity filters and censorship that bias returns.

We examine the ability of structural models to predict credit spreads using global default data and security‐level credit spread data in eight developed economies. We find that two representative, pure default‐risk models tend to underpredict the average credit spreads on investment‐grade (IG) bonds, especially their spreads over government bonds, thereby providing evidence for a “global credit spread puzzle.” However, a model incorporating endogenous liquidity in the secondary debt market helps mitigate the puzzle. Furthermore, the model captures certain determinants of corporate bond market frictions across the eight economies and substantially improves the cross‐sectional fit of individual IG credit spreads.