This book consists of a series of new, peer-reviewed papers in stochastic processes, analysis, filtering and control, with particular emphasis on mathematical finance, actuarial science and engineering. Paper contributors include colleagues, collaborators and former students of Robert Elliott, many of whom are world-leading experts and have made fundamental and significant contributions to these areas.This book provides new important insights and results by eminent researchers in the considered areas, which will be of interest to researchers and practitioners. The topics considered will be diverse in applications, and will provide contemporary approaches to the problems considered. The areas considered are rapidly evolving. This volume will contribute to their development, and present the current state-of-the-art stochastic processes, analysis, filtering and control.Contributing authors include: H Albrecher, T Bielecki, F Dufour, M Jeanblanc, I Karatzas, H-H Kuo, A Melnikov, E Platen, G Yin, Q Zhang, C Chiarella, W Fleming, D Madan, R Mamon, J Yan, V Krishnamurthy.

This book consists of a series of new, peer-reviewed papers in stochastic processes, analysis, filtering and control, with particular emphasis on mathematical finance, actuarial science and engineering. Paper contributors include colleagues, collaborators and former students of Robert Elliott, many of whom are world-leading experts and have made fundamental and significant contributions to these areas.This book provides new important insights and results by eminent researchers in the considered areas, which will be of interest to researchers and practitioners. The topics considered will be diverse in applications, and will provide contemporary approaches to the problems considered. The areas considered are rapidly evolving. This volume will contribute to their development, and present the current state-of-the-art stochastic processes, analysis, filtering and control.Contributing authors include: H Albrecher, T Bielecki, F Dufour, M Jeanblanc, I Karatzas, H-H Kuo, A Melnikov, E Platen, G Yin, Q Zhang, C Chiarella, W Fleming, D Madan, R Mamon, J Yan, V Krishnamurthy.

This book presents the state of the art in the relatively new field of dynamic economic modelling with regime switches. The contributions, written by prominent scholars in the field, focus on dynamic decision problems with regime changes in underlying dynamics or objectives. Such changes can be externally driven or internally induced by decisions. Utilising the most advanced mathematical methods in optimal control and dynamic game theory, the authors address a broad range of topics, including capital accumulation, innovations, financial decisions, population economics, environmental and resource economics, institutional change and the dynamics of addiction. Given its scope, the book will appeal to all scholars interested in mathematical and quantitative economics.

This volume is a collection of chapters covering the latest developments in applications of financial mathematics and statistics to topics in energy, commodity financial markets and environmental economics. The research presented is based on the presentations and discussions that took place during the Fields Institute Focus Program on Commodities, Energy and Environmental Finance in August 2013. The authors include applied mathematicians, economists and industry practitioners, providing for a multi-disciplinary spectrum of perspectives on the subject. The volume consists of four sections: Electricity Markets; Real Options; Trading in Commodity Markets; and Oligopolistic Models for Energy Production. Taken together, the chapters give a comprehensive summary of the current state of the art in quantitative analysis of commodities and energy finance. The topics covered include structural models of electricity markets, financialization of commodities, valuation of commodity real options, game-theory analysis of exhaustible resource management and analysis of commodity ETFs. The volume also includes two survey articles that provide a source for new researchers interested in getting into these topics.

Covering formulation, algorithms, and structural results, and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. Bringing together research from across the literature, the book provides an introduction to nonlinear filtering followed by a systematic development of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP have a threshold optimal policy? When are myopic policies optimal? How do local and global decision makers interact in adaptive decision making in multi-agent social learning where there is herding and data incest? And how can sophisticated radars and sensors adapt their sensing in real time?

Every day we need to solve large problems for which supercomputers are needed. High performance computing (HPC) is a paradigm that allows to efficiently implement large-scale computational tasks on powerful supercomputers unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many challenging real world problems arising in engineering, economics, medicine and other areas can be formulated as large-scale computational tasks. The volume is a comprehensive collection of extended contributions from the High performance computing conference held in Borovets, Bulgaria, September 2019. This book presents recent advances in high performance computing. The topics of interest included into this volume are: HP software tools, Parallel Algorithms and Scalability, HPC in Big Data analytics, Modelling, Simulation & Optimization in a Data Rich Environment, Advanced numerical methods for HPC, Hybrid parallel or distributed algorithms. The volume is focused on important large-scale applications like Environmental and Climate Modeling, Computational Chemistry and Heuristic Algorithms.

Insurance Planning Models: Price Competition and Regulation of Financial Stability is an exciting new book that takes readers inside the secrets of internal organization of the modern general insurance business. Many people know that it is subject to intensive state regulation, whereby the purpose is to maintain long-term efficiency, honesty, security and stability in the interest and for the protection of policyholders. However, except for knowing that the insurance system is regulated by intensive calculations, that the insurance companies have different positions on the market, that they pursue different goals and even compete with each other, and that one of the tools of this competition is the policy price, not so many people know how to achieve these deserving goals.In developing quantitative recommendations and directives to competing insurers, regulators rely on certain models. In the 1900s, such models were proposed. They were useful for an insight into the probabilistic nature of the insurance process, but not for direct application to practically meaningful problems of insurance regulation. This book is your guide to the rigorously constructed long-term dynamic models with the aim to improve regulatory methods and develop quantitative recommendations using both analytical calculations and computer simulation. It is addressed to a wide range of readers, including interested policyholders, economists whose interest lies in insurance management and regulation, and mathematicians wishing to expand the scope of application for their knowledge.This book is devoted to certain issues that are either not sufficiently presented, or even absent in the literature. It is an attempt to penetrate from the standpoint of mathematical modeling into the goals which face insurance regulators and contending company managers for preventing insolvencies, or even crises pertinent to badly regulated complex reflexive systems.It offers rigorous probabilistic models of long-term insurance business based on the laws of mass phenomena. They mitigate deficiencies of oversimplified risk models. The book presents advances in probabilistic techniques designed to seek quantitative, rather than qualitative, directives and recommendations regarding safe control aiming to achieve different business goals.