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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.
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