Derman and Kani (1994), Dupire (1994), and Rubinstein (1994) hypothesize that asset return volatility is a deterministic function of asset price and time, and develop a deterministic volatility function (DVF) option valuation model that has the potential of fitting the observed cross section of option prices exactly. Using S&P 500 options from June 1988 through December 1993, we examine the predictive and hedging performance of the DVF option valuation model and find it is no better than an ad hoc procedure that merely smooths Black–Scholes (1973) implied volatilities across exercise prices and times to expiration.

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.

Regulation Fair Disclosure (FD), imposed by the Securities and Exchange Commission in October 2000, was designed to prohibit disclosure of material private information to selected market participants. The informational advantage such select participants gain is unclear. If multiple “insiders” receive identical information, private information is immediately incorporated in price and each insider has zero expected profit. If, on the other hand, Regulation FD has curtailed the flow of information from firms, private information becomes longer‐lived and more valuable. Hence, market makers will demand increased compensation by widening the adverse selection component of the bid‐ask spread. We identify the cost components of the bid‐ask spread for a sample of NASDAQ stocks surrounding the implementation of Regulation FD. Controlling for other factors affecting the spread, we find that adverse selection costs increase approximately 36% after Regulation FD. We interpret our finding as Regulation FD failing to achieve one of its desired objectives.

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.