Did CDS Trading Improve the Market for Corporate Bonds?Financial innovation through the creation of new markets and securities impacts related markets as well, changing their efficiency, quality (pricing error) and liquidity. The credit default swap (CDS) market was undoubtedly one of the salient new markets of the past decade. In this paper we examine whether the advent of CDS trading was beneficial to the underlying secondary market for corporate bonds. We employ econometric specifications that account for information across CDS, bond, equity, and volatility markets. We also develop a novel methodology to utilize all observations in our data set even when continuous daily trading is not evidenced, because bonds trade much less frequently than equities. Using an extensive sample of CDS and bond trades over 2002-2008, we find that the advent of CDS was largely detrimental -bond markets became less efficient, evidenced no reduction in pricing errors, and experienced no improvement in liquidity. These findings are robust to various slices of the data set and specification of our tests.
O ur objective is to examine the pricing behavior of corporate convertible bonds that are currently traded in the U.S. secondary convertible bond market. No study to date has used recent data from the U.S. convertible bond market, so our work contributes significantly to an understanding of this market.The model follows the Duffie and Singleton [1999] credit risk model. This approach lets us calibrate default probabilities in the model to the market prices of both risky debt and equity.Our results indicate that convertible bonds with relatively low conversion value are often underpriced to the extent that their prices regularly violate pricing bounds implied by the presence of the conversion option. For such bonds, we find that the boundary condition associated with the bond's straight debt value is often violated, and negative option prices are implied. The differences between actual and model prices for bonds with low conversion value are not the result of biases inherent in the pricing model used but are instead attributable to a systematic underpricing of these bonds in the marketplace.This finding should be of importance to corporate debt issuers and investors alike, in the wake of the tremendous growth in the convertible bond market. 1 I. LITERATURE REVIEWThe convertible bond is a hybrid security that combines straight bond characteristics with a conversion feature. This feature allows the owner to exchange the convertible bond for another security of different characteristics, usually the issuing firm's common stock. Therefore, while it retains most of the characteristics of straight debt, the convertible bond also offers an upside potential associated with the underlying common stock. Given its hybrid nature, valuation of a convertible bond requires a model that captures both its exposure to credit risk and the upside potential from its equity-like behavior.Credit risk models are generally classified in two categories: structural models and reducedform models. 2 Structural models assume the value of a firm is continuous over time and, given the dynamics of firm value through time and appropriate terminal and boundary conditions, derive the value of the firm's debt. Merton [1974] developed one of the first models, which assumes that default is allowed only at the maturity of the debt. Subsequent structural models relax some of the unrealistic assumptions of the Merton [1974] model. 3 Default can instead occur any time during the life of the bond, and default is triggered when the value of the firm reaches a certain threshold level. Problems arising from unobservable variables and complex capital structure still limit the practical application of such models.
Did CDS Trading Improve the Market for Corporate Bonds?Financial innovation through the creation of new markets and securities impacts related markets as well, changing their efficiency, quality (pricing error) and liquidity. The credit default swap (CDS) market was undoubtedly one of the salient new markets of the past decade. In this paper we examine whether the advent of CDS trading was beneficial to the underlying secondary market for corporate bonds. We employ econometric specifications that account for information across CDS, bond, equity, and volatility markets. We also develop a novel methodology to utilize all observations in our data set even when continuous daily trading is not evidenced, because bonds trade much less frequently than equities. Using an extensive sample of CDS and bond trades over 2002-2008, we find that the advent of CDS was largely detrimental -bond markets became less efficient, evidenced no reduction in pricing errors, and experienced no improvement in liquidity. These findings are robust to various slices of the data set and specification of our tests.
In this paper, we introduce regime-switching in a two-factor stochastic volatility (SV) model to explain the behavior of short-term interest rates. We model the volatility of short-term interest rates as a stochastic volatility process whose mean is subject to shifts in regime. We estimate the regime-switching stochastic volatility (RSV) model using a Gibbs Sampling-based Markov Chain Monte Carlo algorithm. In-sample results strongly favor the RSV model in comparison to the single-state SV model and GARCH family of models. Out-of-sample results are mixed and, overall, provide weak support for the RSV model.Key Words: Short-term interest rates, stochastic volatility, regime switching, MCMC methods, GARCH models. JEL Classification: G10, G12a Corresponding Author. We acknowledge the comments of Arthur Warga and seminar participants at the University of Houston, McGill University and the NFA 2000 Meetings in Waterloo. We thank Siddhartha Chib for providing us with very helpful computational tips. We also thank the editor, associated editor and two anonymous referees for their valuable comments.2 Regime -Switching Stochastic Volatility and Short -term Interest RatesAbstract In this paper, we introduce regime-switching in a two-factor stochastic volatility (SV) model to explain the behavior of short-term interest rates. We model the volatility of short-term interest rates as a stochastic volatility process whose mean is subject to shifts in regime. We estimate the regime-switching stochastic volatility (RSV) model using a Gibbs Sampling-based Markov Chain Monte Carlo algorithm. In-sample results strongly favor the RSV model in comparison to the single-state SV model and GARCH family of models. Out-of-sample results are mixed and, overall, provide weak support for the RSV model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.