International Security and Terrorism analyses contemporary issues in international orders. This book assesses the terrorist attacks which were organised in African, Asian, European and Middle Eastern countries in detail. Therefore, it provides a comprehensive understanding for academics, practitioners, researchers, students and readers who are interested in international security and terrorism. It utilises data about casualties, attack types and countries that exposed terrorism. The book critically examines terrorism, traditional counter-terrorism tactics, securitisation and the new terrorism. It focuses on religiously-motivated terrorist groups, e.g. al-Qaeda, ISIS, Boko Haram and al-Shabaab.

Aytaç Kadıoğlu is an assistant professor of politics and international relations at the Middle East Institute, Sakarya University. Kadıoğlu received his BA at Istanbul University, MA in International Relations at the University of Leicester and Ph.D. in International Relations at the University of Nottingham. His expertise includes security studies, conflict resolution, international terrorism and counter-terrorism studies.

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.