Economic cybercrime is one of the unwelcomed consequences of unprecedented intrusion of the Internet in our daily lives. Academic interest in understanding the causes and adverse impacts of cybercrime is growing. Opportunity perspective, which perceives individuals’ daily routines and lifestyles as the primary facilitator of victimization, has been widely applied as a theoretical and conceptual framework in cybercrime victimization research. However, this approach put the responsibility of the victimization on victims explicitly, which hampers the application of crime prevention and reduction efforts. This book presents the Contextual Vulnerabilities approach based on the significant findings of a doctoral study conducted at Durham University in the United Kingdom. Contextual vulnerabilities approach posits that cybercrime victimization is the outcome of the confluence of contextual factors that are mostly beyond victims’ control. This approach presumes that controlling individual (micro-level), macro-level and socio-cultural vulnerabilities may prevent cybercrime victimization. Though the preliminary goal of this book is to provide a new theoretical perspective to scholars researching victimization in cyberspace, I hope the wider audience would find it informative.

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.