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.

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 focuses on identifying and explaining the key determinants of scenario analysis in the context of operational risk, stress testing and systemic risk, as well as management and planning. Each chapter presents alternative solutions to perform reliable scenario analysis. The author also provides technical notes and describes applications and key characteristics for each of the solutions. In addition, the book includes a section to help practitioners interpret the results and adjust them to real-life management activities. Methodologies, including those derived from consensus strategies, extreme value theory, Bayesian networks, Neural networks, Fault Trees, frequentist statistics and data mining are introduced in such a way as to make them understandable to readers without a quantitative background. Particular emphasis is given to the added value of the implementation of these methodologies.

An in-depth guide to global and risk finance based on financial models and data-based issues that confront global financial managers. Globalization, Gating, and Risk Finance offers perspectives on global risk finance in a world with economies in transition. Developed from lectures and research projects investigating the consequences of globalization and strategic approaches to fundamental economics and finance, it provides an approach based on financial models and data; it includes many case-study problems. The book departs from the traditional macroeconomic and financial approaches to global and strategic risk finance, where economic power and geopolitical issues are intermingled to create complex and forward-looking financial systems. Chapter coverage includes: Globalization: Economies in Collision; Data, Measurements, and Global Finance; Global Finance: Utility, Financial Consumption, and Asset Pricing; Macroeconomics, Foreign Exchange, and Global Finance; Foreign Exchange Models and Prices; Asia: Financial Environment and Risks; Financial Currency Pricing, Swaps, Derivatives, and Complete Markets; Credit Risk and International Debt; Globalization and Trade: A Changing World; and Compliance and Financial Regulation. Provides a framework for global financial and inclusive models, some of which are not commonly covered in other books. Considers risk management, utility, and utility-based multi-agent financial theories. Presents a theoretical framework to assist with a variety of problems ranging from derivatives and FX pricing to bond default to trade and strategic regulation. Provides detailed explanations and mathematical proofs to aid the readers’ understanding. Globalization, Gating, and Risk Finance is appropriate as a text for graduate students of global finance, general finance, financial engineering, and international economics, and for practitioners.

"Advanced Quantitative Finance: Trading, Risk, and Portfolio Optimization" unfolds as an essential guide for anyone eager to delve into the sophisticated world of modern finance. This comprehensive text blends theoretical underpinnings with practical insights, offering a robust exploration of the quantitative techniques driving today's markets. Each chapter systematically demystifies complex subjects—from risk management and derivatives pricing to algorithmic trading and asset pricing models—empowering readers to grasp the nuances of financial analysis with clarity and precision. Structured for both novices and seasoned professionals, the book navigates the latest advancements in machine learning, big data analytics, and behavioral finance, presenting them as indispensable tools for the contemporary financial landscape. With a focus on actionable knowledge and strategic applications, readers will gain the proficiency needed to enhance their decision-making, optimize investment portfolios, and effectively manage risk in an ever-evolving economic environment. This book is your invitation to not only understand quantitative finance but to excel in it, unlocking new levels of insight and innovation in your financial pursuits.

This book presents a collection of essays written by leading researchers to honor Roman Słowiński’s major scholarly interests and contributions. He is well-known for conducting extensive research on methodologies and techniques for intelligent decision support, where he combines operational research and artificial intelligence. The book reconstructs his main contributions, presents cutting-edge research and provides an outlook on the most promising and advanced domains of computer science and multiple criteria decision aiding. The respective chapters cover a wide range of related research areas, including decision sciences, ordinal data mining, preference learning and multiple criteria decision aiding, modeling of uncertainty and imprecision in decision problems, rough set theory, fuzzy set theory, multi-objective optimization, project scheduling and decision support applications. As such, the book will appeal to researchers and scholars in related fields.

This book presents a set of new, innovative mathematical modeling tools for analyzing financial risk. Operational Tools in the Management of Financial Risks presents an array of new tools drawn from a variety of research areas, including chaos theory, expert systems, fuzzy sets, neural nets, risk analysis, stochastic programming, and multicriteria decision making. Applications cover, but are not limited to, bankruptcy, credit granting, capital budgeting, corporate performance and viability, portfolio selection/management, and country risk. The book is organized into five sections. The first section applies multivariate data and multicriteria analyses to the problem of portfolio selection. Articles in this section combine classical approaches with newer methods. The second section expands the analysis in the first section to a variety of financial problems: business failure, corporate performance and viability, bankruptcy, etc. The third section examines the mathematical programming techniques including linear, dynamic, and stochastic programming to portfolio managements. The fourth section introduces fuzzy set and artificial intelligence techniques to selected types of financial decisions. The final section explores the contribution of several multicriteria methodologies in the assessment of country financial risk. In total, this book is a systematic examination of an emerging methodology for managing financial risk in business.

Focusing on life insurance and pensions, this book addresses various aspects of modelling in modern insurance: insurance liabilities; asset-liability management; securitization, hedging, and investment strategies. With contributions from internationally renowned academics in actuarial science, finance, and management science and key people in major life insurance and reinsurance companies, there is expert coverage of a wide range of topics, for example: models in life insurance and their roles in decision making; an account of the contemporary history of insurance and life insurance mathematics; choice, calibration, and evaluation of models; documentation and quality checks of data; new insurance regulations and accounting rules; cash flow projection models; economic scenario generators; model uncertainty and model risk; model-based decision-making at line management level; models and behaviour of stakeholders. With author profiles ranging from highly specialized model builders to decision makers at chief executive level, this book should prove a useful resource to students and academics of actuarial science as well as practitioners.

In the evolving landscape of finance, traditional institutions grapple with challenges ranging from outdated processes to limited accessibility, hindering the industry's ability to meet the diverse needs of a modern, digital-first society. Moreover, as the world embraces Decentralized Finance (DeFi) and Artificial Intelligence (AI) technologies, there becomes a need to bridge the gap between innovation and traditional financial systems. This disconnect not only impedes progress but also limits the potential for financial inclusion and sustainable growth. AI-Driven Decentralized Finance and the Future of Finance addresses the complexities and challenges currently facing the financial industry. By exploring the transformative potential of AI in decentralized finance, this book offers a roadmap for navigating the convergence of technology and finance. From optimizing smart contracts to enhancing security and personalizing financial experiences, the book provides practical insights and real-world examples that empower professionals to leverage AI-driven strategies effectively.

This title brings together frontier research on complex economic systems, heterogeneous interacting agents, bounded rationality, and nonlinear dynamics in economics. The book contains the proceedings of the CEF2015 (21st Computing in Economics in Finance), held 20-22 June 2015 in Taipei, Taiwan, and addresses some of the important driving forces for various emergent properties in economies, when viewed as complex systems. The breakthroughs reported in this book are a result of an interdisciplinary approach and simulation remains the unifying theme for these papers as they deal with a wide range of topics in economics. The text is a valuable addition to the efforts in promoting the complex systems view in economic science. The computational experiments reported in the book are both transparent and replicable. Complex System Modeling and Simulation in Economics and Finance is useful for graduate courses of complex systems, with particular focus on economics and finance. At the same time it serves as a good overview for researchers who are interested in the topic.