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This book provides a perspective on a number of financial modelling analytics and risk management. The book begins with extensive outline of GLM estimation techniques combined with the proof of its fundamental results. Applications of static and dynamic models provide a unified approach to the estimation of nonlinear risk models. The book then examines the definition of risks and their management, with particular emphasis on the importance of bi-modal distributions for financial regulation. Chapters also cover the implications of stress testing and the noncyclical CAR (Capital Adequacy Rule). The next section highlights financial modelling analytic approaches and techniques including an overview of memory based financial models, spanning non-memory models, long run and short memory. Applications of these models are used to highlight their variety and their importance to Financial Analytics. Subsequent chapters offer an extensive overview of multi-fractional models and their important applications to Asset price modeling (from Fractional to Multi-fractional Processes), and a look at the binomial pricing model by discussing the effects of memory on the pricing of asset prices. The book concludes with an examination of an algorithmic future perspective to real finance.

The chapters in Future Perspectives in Risk Models and Finance are concerned with both theoretical and practical issues. Theoretically, financial risks models are models of "certainty", based on information and rules that are both available and agree to by their user. Empirical and data finance however, has provided a bridge between theoretical constructs risks models and the empirical evidence that these models entail. Numerous approaches are then used to model financial risk models, emphasizing mathematical and stochastic models based on the fundamental theoretical tenets of finance and others departing from the fundamental assumptions of finance. The underlying mathematical foundations of these risks models provide a future guideline for risk modeling. Both static and dynamic risk models are then considered. The chapters in this book provide selective insights and developments, that can contribute to a greater understanding the complexity of financial modelling and its ability to bridge financial theories and their practice. Risk models are models of uncertainty, and therefore all risk models are an expression of perceptions, priorities, needs and the information we have. In this sense, all risks models are complex hypotheses we have constructed and based on "what we have or believe". Risk models are then challenged by their definition, are risk definition defining in fact prospective risks? By their estimation, what data can we apply to estimate risk processes and how can we do so? How should we use the data and the models at hand for useful and constructive end.