2 resultados para ECONOMIC CRISES
em Aston University Research Archive
Resumo:
This study investigates the impact of a human resource management (HRM) system, which integrates both content and process of human resource (HR) practices, on organizational performance, through collective employee reactions. The analysis is based on a sample of 1,250 Greek employees working in 133 public- and private-sector organizations, which operate in the present context of severe financial and economic crises. The findings of the structural equation modeling suggest that content and process are two inseparable faces of an HRM system that help to reveal a comprehensive picture of the HRM-organizational performance relationship. Based on the findings that collective employee reactions mediate the HRM content (i.e., organizational performance relationship) and HRM process moderates the HRM content (i.e., employee reactions relationship), the study has several theoretical and practice implications. © 2014 Wiley Periodicals, Inc.
Resumo:
We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.