The current world financial scene indicates at an intertwined and interdependent relationship between financial market activity and economic health. This book explains how the economic messages delivered by the dynamic evolution of financial asset returns are strongly related to option prices. The Black Scholes framework is introduced and by underlining its shortcomings, an alternative approach is presented that has emerged over the past ten years of academic research, an approach that is much more grounded on a realistic statistical analysis of data rather than on ad hoc tractable continuous time option pricing models. The reader then learns what it takes to understand and implement these option pricing models based on time series analysis in a self-contained way. The discussion covers modeling choices available to the quantitative analyst, as well as the tools to decide upon a particular model based on the historical datasets of financial returns. The reader is then guided into numerical deduction of option prices from these models and illustrations with real examples are used to reflect the accuracy of the approach using datasets of options on equity indices.

This book provides a perspective on a number of approaches to financial modelling and risk management. It examines both theoretical and practical issues. Theoretically, financial risks models are models of a real and a financial "uncertainty", based on both common and private information and economic theories defining the rules that financial markets comply to. Financial models are thus challenged by their definitions and by a changing financial system fueled by globalization, technology growth, complexity, regulation and the many factors that contribute to rendering financial processes to be continuously questioned and re-assessed. The underlying mathematical foundations of financial risks models provide future guidelines for risk modeling. The book's chapters provide selective insights and developments that can contribute to better understand the complexity of financial modelling and its ability to bridge financial theories and their practice. Future Perspectives in Risk Models and Finance begins with an extensive outline by Alain Bensoussan et al. of GLM estimation techniques combined with proofs of fundamental results. Applications to static and dynamic models provide a unified approach to the estimation of nonlinear risk models. A second section is concerned with the definition of risks and their management. In particular, Guegan and Hassani review a number of risk models definition emphasizing the importance of bi-modal distributions for financial regulation. An additional chapter provides a review of stress testing and their implications. Nassim Taleb and Sandis provide an anti-fragility approach based on "skin in the game". To conclude, Raphael Douady discusses the noncyclical CAR (Capital Adequacy Rule) and their effects of aversion of systemic risks. A third section emphasizes analytic financial modelling approaches and techniques. Tapiero and Vallois provide an overview of mathematical systems and their use in financial modeling. These systems span the fundamental Arrow-Debreu framework underlying financial models of complete markets and subsequently, mathematical systems departing from this framework but yet generalizing their approach to dynamic financial models. Explicitly, models based on fractional calculus, on persistence (short memory) and on entropy-based non-extensiveness. Applications of these models are used to define a modeling approach to incomplete financial models and their potential use as a "measure of incompleteness". Subsequently Bianchi and Pianese provide an extensive overview of multi-fractional models and their important applications to Asset price modeling. Finally, Tapiero and Jinquyi consider the binomial pricing model by discussing the effects of memory on the pricing of asset prices.

The current world financial scene indicates at an intertwined and interdependent relationship between financial market activity and economic health. This book explains how the economic messages delivered by the dynamic evolution of financial asset returns are strongly related to option prices. The Black Scholes framework is introduced and by underlining its shortcomings, an alternative approach is presented that has emerged over the past ten years of academic research, an approach that is much more grounded on a realistic statistical analysis of data rather than on ad hoc tractable continuous time option pricing models. The reader then learns what it takes to understand and implement these option pricing models based on time series analysis in a self-contained way. The discussion covers modeling choices available to the quantitative analyst, as well as the tools to decide upon a particular model based on the historical datasets of financial returns. The reader is then guided into numerical deduction of option prices from these models and illustrations with real examples are used to reflect the accuracy of the approach using datasets of options on equity indices.

This book combines theory and practice to analyze risk measurement from different points of view. The limitations of a model depend on the framework on which it has been built as well as specific assumptions, and risk managers need to be aware of these when assessing risks. The authors investigate the impact of these limitations, propose an alternative way of thinking that challenges traditional assumptions, and also provide novel solutions. Starting with the traditional Value at Risk (VaR) model and its limitations, the book discusses concepts like the expected shortfall, the spectral measure, the use of the spectrum, and the distortion risk measures from both a univariate and a multivariate perspective.

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. "