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Risk management has become one of the key requirements for insightful decision-making. What are risks sources? How are they being managed? This book describes certainty, uncertainty, financial risks, methods of risk mitigation, and risk management. The first chapter of this book represents some milestones in risk management and introduces the main aspects of financial risk management. The following chapters discuss various types of financial risk such as market risk, credit risk, operational risk, liquidity risk, interest rate risk, and other financial risks. The last chapter describes enterprise risk management which binds together all the risks. This book, which is accompanied by PowerPoint presentations, is aimed at lecturers, students, and practitioners with an interest in risk management. The book is the fruit of the authors' long years of work in the field of risk management, serving as a risk management advisor and teaching an MBA-level academic course on the topic for economics and business administration students.
Risk management has become one of the key requirements for insightful decision-making. What are risks sources? How are they being managed? This book describes certainty, uncertainty, financial risks, methods of risk mitigation, and risk management.The first chapter of this book represents some milestones in risk management and introduces the main aspects of financial risk management. The following chapters discuss various types of financial risk such as market risk, credit risk, operational risk, liquidity risk, interest rate risk, and other financial risks. The last chapter describes enterprise risk management which binds together all the risks.This book, which is accompanied by PowerPoint presentations, is aimed at lecturers, students, and practitioners with an interest in risk management. The book is the fruit of the authors' long years of work in the field of risk management, serving as a risk management advisor and teaching an MBA-level academic course on the topic for economics and business administration students.Resources are available to instructors who adopt this book. More details at www.worldscientific.com/worldscibooks/10.1142/13297-sm
Developed over 20 years of teaching academic courses, the Handbook of Financial Risk Management can be divided into two main parts: risk management in the financial sector; and a discussion of the mathematical and statistical tools used in risk management. This comprehensive text offers readers the chance to develop a sound understanding of financial products and the mathematical models that drive them, exploring in detail where the risks are and how to manage them. Key Features: Written by an author with both theoretical and applied experience Ideal resource for students pursuing a master’s degree in finance who want to learn risk management Comprehensive coverage of the key topics in financial risk management Contains 114 exercises, with solutions provided online at www.crcpress.com/9781138501874
The aim of the book is to provide an overview of risk management in life insurance companies. The focus is twofold: (1) to provide a broad view of the different topics needed for risk management and (2) to provide the necessary tools and techniques to concretely apply them in practice. Much emphasis has been put into the presentation of the book so that it presents the theory in a simple but sound manner. The first chapters deal with valuation concepts which are defined and analysed, the emphasis is on understanding the risks in corresponding assets and liabilities such as bonds, shares and also insurance liabilities. In the following chapters risk appetite and key insurance processes and their risks are presented and analysed. This more general treatment is followed by chapters describing asset risks, insurance risks and operational risks - the application of models and reporting of the corresponding risks is central. Next, the risks of insurance companies and of special insurance products are looked at. The aim is to show the intrinsic risks in some particular products and the way they can be analysed. The book finishes with emerging risks and risk management from a regulatory point of view, the standard model of Solvency II and the Swiss Solvency Test are analysed and explained. The book has several mathematical appendices which deal with the basic mathematical tools, e.g. probability theory, stochastic processes, Markov chains and a stochastic life insurance model based on Markov chains. Moreover, the appendices look at the mathematical formulation of abstract valuation concepts such as replicating portfolios, state space deflators, arbitrage free pricing and the valuation of unit linked products with guarantees. The various concepts in the book are supported by tables and figures.
This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.
This book deals with Enterprise Risk Management (ERM) and, in particular, Quantitative Risk Management (QRM) in life insurance business. Constituting a “bridge” between traditional actuarial mathematics and insurance risk management processes, its purpose is to provide advanced undergraduate and graduate students in the Actuarial Sciences, Finance and Economics with the basics of ERM (in general) and QRM applied to life insurance business. The main topics dealt with are: general issues on ERM, risk management tools for life insurance and life annuities, deterministic and stochastic analysis of the behaviour of a portfolio fund, application of sensitivity testing to assess ranges of results of interest, stress testing to assess the impact of extreme scenarios, and the product development process for life annuity products.
There are two types of tenn structure models in the literature: the equilibrium models and the no-arbitrage models. And there are, correspondingly, two types of interest rate derivatives pricing fonnulas based on each type of model of the tenn structure. The no-arbitrage models are characterized by the work of Ho and Lee (1986), Heath, Jarrow, and Morton (1992), Hull and White (1990 and 1993), and Black, Dennan and Toy (1990). Ho and Lee (1986) invent the no-arbitrage approach to the tenn structure modeling in the sense that the model tenn structure can fit the initial (observed) tenn structure of interest rates. There are a number of disadvantages with their model. First, the model describes the whole volatility structure by a sin gle parameter, implying a number of unrealistic features. Furthennore, the model does not incorporate mean reversion. Black-Dennan-Toy (1990) develop a model along tbe lines of Ho and Lee. They eliminate some of the problems of Ho and Lee (1986) but create a new one: for a certain specification of the volatility function, the short rate can be mean-fteeting rather than mean-reverting. Heath, Jarrow and Morton (1992) (HJM) construct a family of continuous models of the term struc ture consistent with the initial tenn structure data.
Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.
Investment and risk management problems are fundamental problems for financial institutions and involve both speculative and hedging decisions. A structured approach to these problems naturally leads one to the field of applied mathematics in order to translate subjective probability beliefs and attitudes towards risk and reward into actual decisions. In Risk and Portfolio Analysis the authors present sound principles and useful methods for making investment and risk management decisions in the presence of hedgeable and non-hedgeable risks using the simplest possible principles, methods, and models that still capture the essential features of the real-world problems. They use rigorous, yet elementary mathematics, avoiding technically advanced approaches which have no clear methodological purpose and are practically irrelevant. The material progresses systematically and topics such as the pricing and hedging of derivative contracts, investment and hedging principles from portfolio theory, and risk measurement and multivariate models from risk management are covered appropriately. The theory is combined with numerous real-world examples that illustrate how the principles, methods, and models can be combined to approach concrete problems and to draw useful conclusions. Exercises are included at the end of the chapters to help reinforce the text and provide insight. This book will serve advanced undergraduate and graduate students, and practitioners in insurance, finance as well as regulators. Prerequisites include undergraduate level courses in linear algebra, analysis, statistics and probability.