Family Business Restructuring: A Review and Research Agenda

Although business restructuring occurs frequently and it is important for the prosperity of family firms across generations, research on family firms has largely evolved separately from research on business restructuring. This is a missed opportunity, since the two domains are complementary, and understanding the context, process, content, and outcome dimensions is relevant to both research streams. We address this by examining the intersection between research on business restructuring and family firms to improve our knowledge of each area and inform future research. To achieve this goal, we review and organize research across different dimensions to create an integrative framework. Building on current research, we focus on 88 studies at the intersection of family firm and business restructuring research to develop a model that identifies research needs and suggests directions for future research.     

Expanding Insights on the Diversity Climate–Performance Link: The Role of Workgroup Discrimination and Group Size

The present study extends knowledge of the performance consequences of workgroup diversity climate. Building upon Kopelman, Brief, and Guzzo's ( 1990 ) climate model of productivity, we introduce workgroup discrimination as a behavioral mediator that explains the positive effects of diversity climate on workgroup performance. In addition, we investigate group size as a moderator upon which this mediated relationship depends. We test these moderated‐mediated propositions using a split‐sample design and data from 248 military workgroups comprising 8,707 respondents. Findings from structural equation modeling reveal that diversity climate is consistently positively related to workgroup performance and that this relationship is mediated by discrimination. Results yield a pattern of moderated mediation, in that the indirect relationship between workgroup diversity climate (through perceptions of workgroup discrimination) and group performance was more pronounced in larger than in smaller workgroups. These results illustrate that discrimination and group size represent key factors in determining how a diversity climate is associated with group performance and, thus, have significant implications for research and practice. © 2014 Wiley Periodicals, Inc.     

PERSISTENCE AND VOLATILITY OF BEVERIDGE CYCLES

This article explains the cyclical behavior of the fluctuations in unemployment and vacancies by demand externalities. Adding such externalities to an otherwise standard search and matching model reduces the need for exogenous shocks in explaining these fluctuations. Under plausible parameter values, the equilibrium dynamics include a stable limit cycle that resembles the empirically observed counterclockwise cycles around the Beveridge curve. Calibrated to the duration of the business cycle, these endogenous “Beveridge cycles” are as persistent as the data, without losing any of the amplification of the standard model.     

SPAC merger announcement returns and subsequent performance

Special purpose acquisition companies (SPACs) are created to raise capital and then find non-listed operating companies with which to merge. While most of the extant research has focused on SPAC initial public offerings, we study what happens when SPACs announce business combinations. Our analysis of 236 ‘deSPACs’ completed between January 2012 and June 2021 in the United States documents an average short-term announcement return of +7.4% and a 1-year abnormal return of −14.1% (−18.0% over 2 years) for public investors beginning from the merger announcement. Short-term returns decrease with longer times from initial public offering until announcement.     

Local volatility under rough volatility

Several asymptotic results for the implied volatility generated by a rough volatility model have been obtained in recent years (notably in the small-maturity regime), providing a better understanding of the shapes of the volatility surface induced by rough volatility models, supporting their calibration power to SP500 option data. Rough volatility models also generate a local volatility surface, via the so-called Markovian projection of the stochastic volatility. We complement the existing results on implied volatility by studying the asymptotic behavior of the local volatility surface generated by a class of rough stochastic volatility models, encompassing the rough Bergomi model. Notably, we observe that the celebrated “1/2 skew rule” linking the short-term at-the-money skew of the implied volatility to the short-term at-the-money skew of the local volatility, a consequence of the celebrated “harmonic mean formula” of [Berestycki et al. (2002). Quantitative Finance, 2, 61–69], is replaced by a new rule: the ratio of the at-the-money implied and local volatility skews tends to the constant $1/(H+3/2)$ (as opposed to the constant 1/2), where H is the regularity index of the underlying instantaneous volatility process.     

General Bayesian time-varying parameter vector autoregressions for modeling government bond yields

US yield curve dynamics are subject to time-variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time-varying parameters and stochastic volatility, which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the data and to carry out model selection, we adopt Bayesian shrinkage priors. Our framework is applied to model the US yield curve. We show that the model forecasts well, and focus on selected in-sample features to analyze determinants of structural breaks in US yield curve dynamics.     

TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES

We develop multivariate time-series models using Bayesian additive regression trees that posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged errors. The error variances can be stable, feature stochastic volatility, or follow a nonparametric specification. We evaluate density and tail forecast performance for a set of U.S. macroeconomic and financial indicators. Our results suggest that the proposed models improve forecast accuracy both overall and in the tails. Another finding is that when allowing for nonlinearities in the conditional mean, heteroskedasticity becomes less important. A scenario analysis reveals nonlinear relations between predictive distributions and financial conditions.