This article uses unique data from the London Stock Exchange to examine how trader anonymity and market liquidity affect dealers' decisions about where to place interdealer trades. During our sample period, dealers could trade with each other in the direct, nonanonymous public market or use one of four anonymous brokered trading systems. Surprisingly, we find that adverse selection is less prevalent in the anonymous brokered markets. We show that this pattern can be explained by the way dealers "price" the adverse selection risk inherent in trading with other dealers. We also relate our findings to recent changes in dealer markets.

Prices of interest rate derivative securities depend crucially on the mean reversion parameters of the underlying diffusions. These parameters are subject to estimation bias when standard methods are used. The estimation bias can be substantial even in very large samples and much more serious than the discretization bias, and it translates into a bias in pricing bond options and other derivative securities that is important in practical work. This article proposes a very general and computationally inexpensive method of bias reduction that is based on Quenouille's (1956; Biometrika, 43, 353-360) jackknife. We show how the method can be applied directly to the options price itself as well as the coefficients in the models. We investigate its performance in a Monte Carlo study. Empirical applications to U.S. dollar swap rates highlight the differences between bond and option prices implied by the jackknife procedure and those implied by the standard approach. These differences are large and suggest that bias reduction in pricing options is important in practical applications.

This article studies the determinants of the success of industry consolidations using a unique sample of firms established at the time of their initial public offering: roll-up IPOs. In these transactions, small, private firms merge into a shell company, which goes public at the same time. These firms deliver poor stock returns; their operating performance mimics that of comparable firms but does not justify their high initial valuations. However, if the managers and owners of the firms included in the transaction remain involved in the business as shareholders and directors, operating and stock price performance improve, and future acquisitions are better received by the market. Higher ownership by the sponsor of the transaction leads to a reduction in performance, consistent with the view that the sponsor's compensation is excessive. These findings highlight the impact of corporate governance on performance.

This article uses unique data from the London Stock Exchange to examine how trader anonymity and market liquidity affect dealers' decisions about where to place interdealer trades. During our sample period, dealers could trade with each other in the direct, nonanonymous public market or use one of four anonymous brokered trading systems. Surprisingly, we find that adverse selection is less prevalent in the anonymous brokered markets. We show that this pattern can be explained by the way dealers “price” the adverse selection risk inherent in trading with other dealers. We also relate our findings to recent changes in dealer markets.

Conference at the University of Michigan, and the AFA meetings for helpful comments and suggestions. We would especially like to acknowledge the insights and contributions that Ken Wiles (AppForge, Inc.) has provided to the paper. Anna Danielova and Andras Marosi provided excellent research assistance. Corporate Governance, Incentives, and Industry Consolidations Several changes, such as the advances in information technology and the advent of outsourcing, led to increases in optimal firm size in many fragmented industries over the last decade. This paper studies the determinants of the success of these industry consolidations using a unique sample of firms that were created at the time of their initial public offering: rollup-up IPOs. In these transactions, many small firms merge into a shell company, which goes public at the same time. This sample allows us to follow firms from the day they were established. We find that these firms deliver poor absolute and relative stock returns, on average. Their operating performance mimics that of other firms of the industry, but does not justify to their high initial valuations. However, the average performance hides substantial cross-sectional differences. If

Prices of interest rate derivative securities depend crucially on the mean reversion parameters of the underlying diffusions. These parameters are subject to estimation bias when standard methods are used. The estimation bias can be substantial even in very large samples and much more serious than the discretization bias, and it translates into a bias in pricing bond options and other derivative securities that is important in practical work. This article proposes a very general and computationally inexpensive method of bias reduction that is based on Quenouille's (1956; Biometrika, 43, 353–360) jackknife. We show how the method can be applied directly to the options price itself as well as the coefficients in the models. We investigate its performance in a Monte Carlo study. Empirical applications to U.S. dollar swap rates highlight the differences between bond and option prices implied by the jackknife procedure and those implied by the standard approach. These differences are large and suggest that bias reduction in pricing options is important in practical applications.