Knowledge that Transforms

We empirically examine whether access to deposits with inelastic rates (core deposits) permits a bank to make contractual agreements with borrowers that are infeasible if the bank must pay market rates for funds. Such access insulates a bank's costs of funds from exogenous shocks, allowing it to insulate its borrowers against exogenous credit shocks. We find that, controlling for loan market competition, banks funded more heavily with core deposits provide more loan rate smoothing in response to exogenous changes in aggregate credit risk. Thus we provide evidence for a novel channel linking bank liabilities to relationship lending.

Statistical model selection criteria provide an informed choice of the model with best external (i.e., out-of-sample) validity. Therefore they guard against overfitting (“data snooping”). We implement several model selection criteria in order to verify recent evidence of predictability in excess stock returns and to determine which variables are valuable predictors. We confirm the presence of in-sample predictability in an international stock market dataset, but discover that even the best prediction models have no out-of-sample forecasting power. The failure to detect out-of-sample predictability is not due to lack of power.

This article provides an analytical solution to the problem of an institution optimally managing the market risk of a given exposure by minimizing its Value‐at‐Risk using options. The optimal hedge consists of a position in a single option whose strike price is independent of the level of expense the institution is willing to incur for its hedging program. This optimal strike price depends on the distribution of the asset exposure, the horizon of the hedge, and the level of protection desired by the institution. Moreover, the costs associated with a suboptimal choice of exercise price are economically significant.

Our main goal is to investigate the question of which interest‐rate options valuation models are better suited to support the management of interest‐rate risk. We use the German market to test seven spot‐rate and forward‐rate models with one and two factors for interest‐rate warrants for the period from 1990 to 1993. We identify a one‐factor forward‐rate model and two spot‐rate models with two factors that are not significantly outperformed by any of the other four models. Further rankings are possible if additional criteria are applied.

In this paper, we point out that the widely used stochastic discount factor (SDF) methodology ignores a fully specified model for asset returns. As a result, it suffers from two potential problems when asset returns follow a linear factor model. The first problem is that the risk premium estimate from the SDF methodology is unreliable. The second problem is that the specification test under the SDF methodology has very low power in detecting misspecified models. Traditional methodologies typically incorporate a fully specified model for asset returns, and they can perform substantially better than the SDF methodology.

We model the role various forms of nonrecourse secured debt play in efficiently redeploying assets whose value is state‐specific. Ex ante, an entrepreneur and an asset redeployer make noncontractible state‐specific investments in the primary and next‐best uses of an asset, respectively. The redeployer provides a secured nonrecourse loan equal to the value of the asset in the critical state that separates the good and bad states. In the event of a bad state, this contract averts ex post bargaining over the asset's quasi‐rents on redeployment and leaves the parties' ex ante investments undistorted.

Like security prices, retail deposit interest rates cluster around integers and “even” fractions. However, explanations for security price clustering are incompatible with deposit rate clustering. A theory based on the limited recall of retail depositors is proposed. It predicts that banks tend to set rates at integers and that rates are “sticky” at these levels. The propensity for integer rates increases with the level of wholesale interest rates and deposit market concentration. When banks set noninteger rates, rates are more likely to be just above, rather than just below, integers. The paper finds substantial empirical support for the theory's implications.

This study assesses the stock return performance of 131 firms emerging from Chapter 11. Using differing estimates of expected returns, we consistently find evidence of large, positive excess returns in 200 days of returns following emergence. We also examine the reaction of our sample firms' equity returns to their earnings announcements after emergence from Chapter 11. The positive and significant reactions suggest that our results are driven by the market's expectational errors, not mismeasurement of risk. The results provide an interesting contrast, but not a contradiction, to previous work that has documented poor operating performance for firms emerging from Chapter 11.

Recent turmoil in international financial markets has raised a set of fundamental questions for the global community: Is the set of international financial arrangements, established after the Great Depression and World War II and modified after the abandonment of the gold standard in 1973, up to the challenges of the twenty-first century? Are minor modifications such as slight changes in the governance of the international financial institutions, increased transparency, or surveillance! all that is required to adapt these institutions to the needs of modern economies, or are more fundamental changes necessary? Today, although much has been proposed, discussed, and argued, no consensus on desirable changes has yet been reached. In the meantime, what can countries, especially the poor, the small, and the less developed, do to protect themselves from the seeming ravages of storms brought on by international financial instability?

In this paper, we develop an efficient lattice algorithm to price European and American options under discrete time GARCH processes. We show that this algorithm is easily extended to price options under generalized GARCH processes, with many of the existing stochastic volatility bivariate diffusion models appearing as limiting cases. We establish one unifying algorithm that can price options under almost all existing GARCH specifications as well as under a large family of bivariate diffusions in which volatility follows its own, perhaps correlated, process.