This study, which is based on a PhD thesis; examines, with an integrative perspective, the effects of employees’ perceptions about the empowering behavior of their immediate supervisor on employees’ psychological empowerment, and the effects of employees’ psychological empowerment on their job satisfaction, extrinsic reward satisfaction, affective commitment towards their organizations, turnover intentions; and task performance and organizational citizenship behavior. In addition, this study makes comparisons between the strength of some of the hypothesized relationships.

The primary finding of the study is that there is a strong relationship between enhancing meaningfulness- providing autonomy dimension of empowering behavior of supervisors and employees’ psychological empowerment. Another important finding is that there are positive relationships between empowering behavior of supervisors and employees’ job satisfaction, extrinsic reward satisfaction, and affective commitment.

The results also indicate that the impact of empowerment on job satisfaction is stronger than that on extrinsic reward satisfaction, and the impact of job satisfaction on turnover intention is stronger than that of extrinsic reward satisfaction. Finally, there are negative influences of empowerment on employees’ task performance and organizational citizenship behavior.

Financial time series including high frequency structures like jumps, spikes and stochastic volatility are usually modeled in an ad-hoc manner by stochastic differential equations together with Levy processes. Estimating the parameters and determining the jump size distributions do not have a precise and universally accepted method. Under these circumstances, complexities and confusions usually arise. This book, approaches to this issue from a very different angle through introducing an autocorrelation one process together with finite number of Fourier series terms. Introduction of Fourier series to estimate the dynamics of the process is not done in an ad-hoc manner or as done before in dealing with seasonality. Here the moving average is transformed to a “moving and fluctuating” average by the help of Fourier series. Instead of adding jump structures to the model which makes the parameter estimation quite cumbersome, our model in discrete time can easily be transformed to a well-known mean reverting continuous time process. Moreover, our alternative model turned out to be a quite powerful and accurate forecasting technique.