We analyze why companies that receive private equity investments outperform their rivals. We show that rivals experience a decrease in their stock prices and their operating performance around private equity (PE) investments in their industry.
Applied financial econometrics subjects are featured in this second volume, with papers that survey important research even as they make unique empirical contributions to the literature. These subjects are familiar: portfolio choice, trading volume, the risk-return tradeoff, option pricing, bond yields, and the management, supervision, and measurement of extreme and infrequent risks.
We model the threat of such liquidation through the intermediation of an activist shareholder. Among other things, our model predicts that MDPs are more likely to be adopted by funds that appear to be less effective in providing portfolio services to their investors and that are relatively easy to liquidate or ‘attack’. We test the model on a panel of 236 CEFs and find good agreement with our model.
This study explores the process of organizational change by examining localized social learning in organizational subunits. Specifically, we examine participation in university technology transfer, a new organizational initiative, by tracking 1,780 faculty members, examining their backgrounds and work environments, and following their engagement with academic entrepreneurship.
Multinational companies increasingly rely upon the work of virtual teams to manage their global intellectual assets and encourage innovation. Spanning functional, geographical and corporate boundaries, virtual team members work together on various projects but are based in different locations nationwide or worldwide.
We propose a class of two factor dynamic models for duration data and related risk analysis in finance and insurance. Empirical findings suggest that the conditional mean and (under) overdispersion of times elapsed between stock trades feature various patterns of temporal dependence.
This paper evaluates the role of various volatility specifications, such as multiple stochastic volatility (SV) factors and jump components, in appropriate modeling of equity return distributions.
Simulation-based estimation methods have become more widely used in recent years. We propose a set of tests for structural change in models estimated via simulated method of moments (see Duffe and Singleton (Econometrica 61 (1993) 929).
To enhance our understanding of emerging markets, we study a data set from the Casablanca stock exchange containing all the transaction records over a long span. The exchange was included in 1996 in the International Finance Corporation (IFC) data base roughly 3 years after important market reforms.
We examine several autoregressive-based estimators for the parameters of a moving average process, including the estimators initially proposed by Galbraith and Zinde-Walsh  and Gouriéroux, Monfort and Renault . We also propose over-identified asymptotic-least-squares based variants of the former, and extensions of the latter based on Gallant and Tauchen's  simulated method of moments.
Time series are demeaned when sample autocorrelation functions are computed. By the same logic it would seem appealing to remove seasonal means from seasonal time series before computing sample autocorrelation functions. Yet, standard practice is only to remove the overall mean and ignore the possibility of seasonal mean shifts in the data.
The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data‐driven volatility estimators using high‐frequency data.
We use panel data on ISO 9000 quality certification in 85 countries between 1993 and 1998 to better understand the cross-national diffusion of an organizational practice. Following neoinstitutional theory, we focus on the coercive, normative, and mimetic effects that result from the exposure of firms in a given country to a powerful source of critical resources, a common pool of relevant technical knowledge, and the experiences of firms located in other countries. We use social network theory to develop a systematic conceptual understanding of how firms located in different countries influence each other's rates of adoption as a result of cohesive and equivalent network relationships.
In this paper we examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules that mimic monetary policymaking decisions. Our approach is to build real-time data sets and simulate a real-time policy-setting environment in which we assume that policy is captured by movements in the actual federal funds rate, and then to assess what sorts of policy rule models and what sorts of data best explain what the Federal Reserve actually did.