Firm‐specific human capital investments as a signal of general value: Revisiting assumptions about human capital and how it is managed

Research summary : Prior scholarship has assumed that firm‐specific and general human capital can be analyzed separately. This article argues that, in some settings, this is not the case because prior firm‐specific human capital investments can be a market signal of an individual's willingness and ability to make such investments in the future. As such, the willingness and ability to make firm‐specific investments is a type of general human capital that links firm‐specific and general human capital in important ways. The article develops theory about these investments, market signals, and value appropriation. Then, the article examines implications for human resource management and several important questions in the field of strategic management, including theories of the firm and microfoundations of competitive advantage. Managerial summary : While managers don't often use the terms firm‐specific and general skills, they certainly recognize that investments employees make in their skill sets are more or less relevant to a specific firm. For instance, investing in specific relationships within a firm or learning a firm's proprietary software would be considered firm‐specific investments. While such skills may seem relevant only to the particular firm in which they were invested, these investments may also send valuable signals to competing firms that such employees are willing and able to make similar investments elsewhere. Hence, managers should be interested in determining if a potential hire has made prior firm‐specific investments to help them know whether that person might be likely to make such investments in his or her future place of employment. Copyright © 2016 John Wiley & Sons, Ltd.     

Threshold bipower variation and the impact of jumps on volatility forecasting

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous components using estimators which are not only consistent, but also scarcely plagued by small sample bias. With the aim of achieving this, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower variation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic variation in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S&P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump.     

Combination schemes for turning point predictions

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions generated by autoregressive (AR) and Markov-switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach for both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and the Euro area business cycles.     

The contribution of genetics to milk composition: evidence from Spain

This article focuses on the role that genetic progress may play in improving milk quality. Despite important genetic advances in dairy production, the absence of genetic records in farm management databases has precluded empirical production models from explicitly accounting for differences in genetics across herds. The influence of genetics on milk composition is analyzed by splitting milk production into protein, fat, and other components. The article explores some modeling issues associated with the specification of the effect of genetics in this multi‐output technology framework. In particular, genetic indexes are considered as allocable inputs and the remaining inputs as nonallocable. Our results show that genetics have a significant impact on milk composition. In particular, we find that farmers’ income increases by 6.6% when genetic indexes are augmented by one sample standard deviation.     

Monotone dependence in graphical models for multivariate Markov chains

We show that a deeper insight into the relations among marginal processes of a multivariate Markov chain can be gained by testing hypotheses of Granger noncausality, contemporaneous independence and monotone dependence. Granger noncausality and contemporaneous independence conditions are read off a mixed graph, and the dependence of an univariate component of the chain on its parents—according to the graph terminology—is described in terms of stochastic dominance criteria. The examined hypotheses are proven to be equivalent to equality and inequality constraints on some parameters of a multivariate logistic model for the transition probabilities. The introduced hypotheses are tested on real categorical time series.     

The Board of Directors and The Adoption of Quality Management Tools

Abstract

The article examines the role of the board of directors in Local Public Utilities (LPUs). It aims at verifying empirically if a correlation exists between specific characteristics of the board of directors and the adoption of innovative arrangements addressing emerging needs of users and citizens (i.e. quality). By means of applying multivariate statistical methods to a random sample of sixty Italian LPUs, this study finds the relational capital of the boards affecting the take up of quality-oriented actions by LPUs. These results support the resource-dependence theory, neglected by mainstream literature.     

Using “Rapid Response” as a competitive tool

New technology allows companies to respond quickly to customer demands that differ from country to country. In the international arena, time and flexibility are competitive weapons.     

The Italian hedge funds industry: An empirical analysis of performance and persistence

This study analyses Italian hedge funds performance and persistence. The peculiarity of the Italian hedge fund industry is that 95% of the hedge funds are funds of hedge funds (FoHF), whereas only 5% of them employ other investment styles. Using monthly data on FoHF provided by MondoHedge, we examine the impact of both market variables and funds’ own characteristics on funds performance using panel data. We find that the European, the Japanese and the emerging markets equity markets, and the commodity market have a positive impact on Italian FoHF performances, while the US Bond Market negatively affects them. Moreover, we find performance fees and notice days to have a negative impact on funds performances. Finally, we test the presence of performance persistence. Employing two different nonparametric methods, we find that funds performances are persistent on a monthly and quarterly basis, while the regression-based parametric method provides evidence of persistence only on a monthly basis.