Abstract. I examine how an internal auditor, called the firm, designs a control system for a strategic employee who conditions his thefts on the amount and types of controls. Society sets minimum testing amounts and fines for detected theft, whereas the firm determines the employee's wages and the amount of monitoring above the minimum. The results fall into three separate cases. When society's minimum testing standards and fines are sufficiently high, the employee never steals in any period. In this case, the firm performs the minimum amount of testing and pays the lowest feasible wage. In the remaining two cases, the testing standard and fines are too low to prevent theft by themselves. In these two cases the firm's control system determines whether there will be theft in the first period. I show that if the firm chooses to prevent all first‐period theft, then it uses only one type of control. She offers a wage premium and monitors the minimum amount. The wage premium substitutes for a tine large enough to prevent all theft. If the firm designs controls that do not prevent all theft, then the firm also uses only one control. In contrast to the no‐theft case, the firm pays the lowest feasible wage and monitors above the minimum. This choice reflects the increasing returns to scale of monitoring in preventing theft.

Abstract. Accounting research contains two distinct approaches to the interaction between accounting management and the independent auditor. Game theory suggests that the auditor's testing strategy will affect the manager's reporting strategy and that the two strategies form an equilibrium. The game‐theoretic approach views the auditor as active, in that the auditor acknowledges the effect that his or her testing strategy has on the manager's reporting. In contrast, in the decision‐theoretic approach, the auditor tests reports, but ignores the effect that such testing might have on the manager's reporting behavior. Essentially, the decision‐theoretic approach views the auditor as passive, taking the reporting strategy as given when designing tests. We use United Kingdom data to estimate both models and test their validity using nested hypothesis tests. Our results demonstrate that the active, game‐theoretic model better describes the auditor‐manager interaction. This is the first empirical validation of the game‐theoretic model using archival accounting data.

In this article, the authors develop alternative ways to compare asset pricing models when it is understood that their implied stochastic discount factors do not price all portfolios correctly. Unlike comparisons based on chi square statistics associated with null hypotheses that models are correct, the authors' measures of model performance do not reward variability of discount factor proxies. One of their measures is designed to exploit fully the implications of arbitrage-free pricing of derivative claims. The authors demonstrate empirically the usefulness of their methods in assessing some alternative stochastic factor models that have been proposed in asset pricing literature.

In this article we characterize and estimate the process for short-term interest rates using federal funds interest rate data. We presume that we are observing a discrete-time sample of a stationary scalar diffusion. We concentrate on a class of models in which the local volatility elasticity is constant and the drift has a flexible specification. To accommodate missing observations and to break the link between "economic time" and calendar time, we model the sampling scheme as an increasing process that is not directly observed. We propose and implement two new methods for estimation. We find evidence for a volatility elasticity between one and one-half and two. When interest rates are high, local mean reversion is small and the mechanism for inducing stationarity is the increased volatility of the diffusion process.

ABSTRACT In this article we develop alternative ways to compare asset pricing models when it is understood that their implied stochastic discount factors do not price all portfolios correctly. Unlike comparisons based on statistics associated with null hypotheses that models are correct, our measures of model performance do not reward variability of discount factor proxies. One of our measures is designed to exploit fully the implications of arbitrage‐free pricing of derivative claims. We demonstrate empirically the usefulness of our methods in assessing some alternative stochastic factor models that have been proposed in asset pricing literature.

In this article we develop alternative ways to compare asset pricing models when it is understood that their implied stochastic discount factors do not price all portfolios correctly. Unlike comparisons based on χ 2 statistics associated with null hypotheses that models are correct, our measures of model performance do not reward variability of discount factor proxies. One of our measures is designed to exploit fully the implications of arbitrage-free pricing of derivative claims. We demonstrate empirically the usefulness of our methods in assessing some alternative stochastic factor models that have been proposed in asset pricing literature.

In this article we characterize and estimate the process for short-term interest rates using federal funds interest rate data. We presume that we are observing a discrete-time sample of a stationary scalar diffusion. We concentrate on a class of models in which the local volatility elasticity is constant and the drift has a flexible specification. To accommodate missing observations and to break the link between “economic time” and calendar time, we model the sampling scheme as an increasing process that is not directly observed. We propose and implement two new methods for estimation. We find evidence for a volatility elasticity between one and one-half and two. When interest rates are high, local mean reversion is small and the mechanism for inducing stationarity is the increased volatility of the diffusion process.