Knowledge that Transforms

Cross-country bank lending appears to be subject to market imperfections leading to persistent interest rate differentials. In a model where banks need to cope with liquidity shocks by borrowing or by liquidating assets, we study the scope for international interbank market integration with unsecured lending when cross-country information is noisy. We find that an equilibrium with integrated markets need not always exist, and that it may coexist with one characterized by segmentation. A repo market reduces interest rate spreads and improves upon the segmentation equilibrium. However, it may destroy the unsecured integrated equilibrium.

This paper explores liquidity movements in stock and Treasury bond markets over a period of more than 1800 trading days. Cross-market dynamics in liquidity are documented by estimating a vector autoregressive model for liquidity (that is, bid-ask spreads and depth), returns, volatility, and order flow in the stock and bond markets. We find that a shock to quoted spreads in one market affects the spreads in both markets, and that return volatility is an important driver of liquidity. Innovations to stock and bond market liquidity and volatility prove to be significantly correlated, suggesting that common factors drive liquidity and volatility in both markets. Monetary expansion increases equity market liquidity during periods of financial crises, and unexpected increases (decreases) in the federal funds rate lead to decreases (increases) in liquidity and increases (decreases) in stock and bond volatility. Finally, we find that flows to the stock and government bond sectors play an important role in forecasting stock and bond liquidity. The results establish a link between 'macro' liquidity, or money flows, and 'micro' or transactions liquidity.

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 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.

Empirically, a price increase accompanies the announcement of an open-market stock repurchase program, even though the announcement is not a commitment. In fact, for many announced programs no shares are ever actually repurchased. This article explores this puzzle. In the single-firm-type version of the model, the option that a firm grants itself by announcing a program does not generate announcement returns. In equilibrium, long-run gains from the informed trading that the option creates are offset by short-run costs from the market's accounting for this adverse selection. Based on this trade-off, I construct a signaling (two-type) model that can deliver announcement returns. In the separating equilibrium, good firms do not incur any cost when they announce programs. Their gains from informed trading in the long run offset the cost of announcement incurred in the short run. Mimicry is costly, because a bad firm's long-run gains from informed trading cannot compensate for the short-run cost of announcing.

I show that when an issuer has superior information about the value of its assets, it is better off selling assets separately rather than as a pool due to the information destruction effect of pooling. If, however, the issuer can create a derivative security that is collateralized by the assets, pooling and “tranching” may be optimal. If the residual risk of each asset is not highly correlated, tranching allows the issuer to exploit the risk diversification effect of pooling to create a low-risk and highly liquid security. In contrast, for an uninformed seller, pure pooling reduces underpricing and is preferred to separate asset sales. These results lead to a dynamic model of financial intermediation: originators sell pools of assets, some of which are purchased by informed intermediaries who then further pool and tranche them. Pooling and tranching allow intermediaries to leverage their capital more efficiently, enhancing the returns to their private information.

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.