This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.

The challenges of the current financial environment have revealed the need for a new generation of professionals who combine training in traditional finance disciplines with an understanding of sophisticated quantitative and analytical tools. Risk Management and Simulation shows how simulation modeling and analysis can help you solve risk management problems related to market, credit, operational, business, and strategic risk. Simulation models and methodologies offer an effective way to address many of these problems and are easy for finance professionals to understand and use. Drawing on the author's extensive teaching experience, this accessible book walks you through the concepts, models, and computational techniques. How Simulation Models Can Help You Manage Risk More Effectively Organized into four parts, the book begins with the concepts and framework for risk management. It then introduces the modeling and computational techniques for solving risk management problems, from model development, verification, and validation to designing simulation experiments and conducting appropriate output analysis. The third part of the book delves into specific issues of risk management in a range of risk types. These include market risk, equity risk, interest rate risk, commodity risk, currency risk, credit risk, liquidity risk, and strategic, business, and operational risks. The author also examines insurance as a mechanism for risk management and risk transfer. The final part of the book explores advanced concepts and techniques. The book contains extensive review questions and detailed quantitative or computational exercises in all chapters. Use of MATLAB (R) mathematical software is encouraged and suggestions for MATLAB functions are provided throughout. Learn Step by Step, from Basic Concepts to More Complex Models Packed with applied examples and exercises,

The challenges of the current financial environment have revealed the need for a new generation of professionals who combine training in traditional finance disciplines with an understanding of sophisticated quantitative and analytical tools. Risk Management and Simulation shows how simulation modeling and analysis can help you solve risk management problems related to market, credit, operational, business, and strategic risk. Simulation models and methodologies offer an effective way to address many of these problems and are easy for finance professionals to understand and use. Drawing on the author s extensive teaching experience, this accessible book walks you through the concepts, models, and computational techniques.

How Simulation Models Can Help You Manage Risk More Effectively

Organized into four parts, the book begins with the concepts and framework for risk management. It then introduces the modeling and computational techniques for solving risk management problems, from model development, verification, and validation to designing simulation experiments and conducting appropriate output analysis. The third part of the book delves into specific issues of risk management in a range of risk types. These include market risk, equity risk, interest rate risk, commodity risk, currency risk, credit risk, liquidity risk, and strategic, business, and operational risks. The author also examines insurance as a mechanism for risk management and risk transfer. The final part of the book explores advanced concepts and techniques. The book contains extensive review questions and detailed quantitative or computational exercises in all chapters. Use of MATLAB(r) mathematical software is encouraged and suggestions for MATLAB functions are provided throughout.

Learn Step by Step, from Basic Concepts to More Complex Models

Packed with applied examples and exercises, this book builds from elementary models for risk to more sophisticated, dynamic models for risks that evolve over time. A comprehensive introduction to simulation modeling and analysis for risk management, it gives you the tools to better assess and manage the impact of risk in your organizations. The book can also serve as a support reference for readers preparing for CFA exams, GARP FRM exams, PRMIA PRM exams, and actuarial exams.

This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.