This paper addresses the problem of testing financial models in the presence of market microstructure effects. The moment restrictions implied by the financial and market microstructure models are jointly tested using Hansen’s (1982) GMM approach. To illustrate the methodology, I consider the random walk model in combination with the bid-ask price effect model of Blume and Stambaugh (1983). Within this sufficiently simple framework, I obtain closed-form expressions for the estimators, standard errors of the estimators, and the test statistic, which affords an opportunity to examine the precision of the estimators and the power of the test as the return interval increases. I show that apparent rejections of the random walk model cannot be sustained when tests of the model are adjusted for market microstructure effects, and I discuss other applications of the methodology.

Matthew Richardson, Tom Smith, A Direct Test of the Mixture of Distributions Hypothesis: Measuring the Daily Flow of Information, The Journal of Financial and Quantitative Analysis, Vol. 29, No. 1 (Mar., 1994), pp. 101-116

A general approach to testing serial dependence restrictions implied from financial models is developed. In particular, we discuss joint serial dependence restrictions imposed by random walk, market microstructure, and rational expectations models recently examined in the literature. This approach incorporates more information from the data by explicitly modeling dependencies induced by the use of overlapping observations. Because the estimation problem is sufficiently simple in this framework, the test statistics have simple representations in terms of only a few unknown parameters. As a result, relatively good size properties are attained in small samples. In addition, the benefit to overlapping observations and the advantage of examining multiperiod time series are explicitly quantified.

The need to understand and measure the determinants of market maker bid/ask spreads is crucial in evaluating the merits of competing market structures and the fairness of market maker rents. This study develops a simple, parsimonious model for the market maker's spread that accounts for the effects of price discreteness induced by minimum tick size, order-processing costs, inventory-holding costs, adverse selection, and competition. The inventory-holding and adverse selection cost components of spread are modeled as an option with a stochastic time to expiration. This inventory-holding premium embedded in the spread represents compensation for the price risk borne by the market maker while the security is held in inventory. The premium is partitioned in such a way that the inventory-holding and adverse selection cost components, as well as the probability of an informed trade, are identified. The model is tested empirically using Nasdaq stocks in three distinct minimum tick size regimes and is shown to perform well both in an absolute sense and relative to competing specifications.

This paper reexamines existing evidence regarding the monotonicity of the term premium. Using a recently developed approach for testing inequality constraints, we propose and conduct tests for whether the term premium is monotonic and reach different conclusions from those implied by individual t-statistics on term premiums (even under a Bonferroni-type adjustment). Our results generally support McCulloch's (1987) view that the liquidity preference hypothesis remains unrefuted.

The development of asset pricing models that rely on instrumental variables together with the increased availability of easily‐accessible economic time‐series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness‐of‐fit are inappropriate. This study investigates the distribution of the maximal when k of m regressors are used to predict security returns. We provide a simple procedure that adjusts critical values to account for selecting variables by searching among potential regressors.

The development of asset pricing models that rely on instrumental variables together with the increased availability of easily-accessible economic time-series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness-of-fit are inappropriate. This study investigates the distribution of the maximal R2 when k of m regressors are used to predict security returns. We provide a simple procedure that adjusts critical R2 values to account for selecting variables by searching among potential regressors.

In this paper, we use call option prices to identify synergies and news from merger and acquisition (M&A) transaction announcements. We find that M&A announcements result in large and approximately equal gains to the bidder and the target on average, with the combined gains being large enough to justify the premium paid to target shareholders. On average, M&A announcements release good news about targets, but bad news about bidders. This suggests that market prices understate true synergy gains, and helps reconcile the generally negative market-based evidence on value-creation in takeovers with their continued prominence in everyday business strategy.

We provide a formal test of the liquidity preference hypothesis (LPH), that is, the monotonicity of ex ante term premiums, using nonparametric estimates that do not require a structural model for conditional expected returns. Although the point estimates of the term premiums are consistent with previous conclusions in the literature regarding violations of the LPH, the test statistics are generally insignificant, even when powerful conditioning information is used. These results illustrate the importance of correctly accounting for correlations across maturities and of formally testing the inequality restrictions implied by the LPH.

This paper provides a method for testing for regime differences when regimes are long-lasting. Standard testing procedures are generally inappropriate because regime persistence causes a spurious regression problem – a problem that has led to incorrect inference in a broad range of studies involving regimes representing political, business, and seasonal cycles. The paper outlines analytically how standard estimators can be adjusted for regime dummy variable persistence. While the adjustments are helpful asymptotically, spurious regression remains a problem in small samples and must be addressed using simulation or bootstrap procedures. We provide a simulation procedure for testing hypotheses in situations where an independent variable in a time-series regression is a persistent regime dummy variable. We also develop a procedure for testing hypotheses in situations where the dependent variable has similar properties.