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This book gives an overview of evaluation of the most widespread Value at Risk (VaR)Models in use in most of risk management departments across the financial industry.Value at Risk (VaR) has become the standard measure that financial analysts use to quantify market risk. VaR is defined as the maximum potential change in value of a portfolio of financial instruments with a given probability over a certain horizon. VaR measures can have many applications, such as in risk management, to evaluate the performance of risk takers and for regulatory requirements, and hence it is very important to develop methodologies that provide accurate estimates.The main objective of this book is to survey the most popular univariate VaR methodologies, paying particular attention to their underlying assumptions. The great popularity that this instrument has achieved is essentially due to its conceptual simplicity: VaR reduces the (market) risk associated with any portfolio to just one number, the loss associated to a given probability. VaR can also be applied to governance of endowments, trusts, and pension plans. Essentially trustees adopt portfolio VaR metrics for the entire pooled account.
This book is a one-stop-shop reference for risk management practitioners involved in the validation of risk models. It is a comprehensive manual about the tools, techniques and processes to be followed, focused on all the models that are relevant in the capital requirements and supervisory review of large international banks.
Seminar paper from the year 2004 in the subject Business economics - Controlling, grade: 1,7, European Business School - International University Schloß Reichartshausen Oestrich-Winkel (Department of Accounting and Control), language: English, abstract: The risk and return framework is generally accepted and discussed by scientists, at least since Markowitz introduced his Portfolio Theory in 1952. Subsequently, models were developed to evaluate investments under consideration of risk and return. Traditionally, practitioners primarily focused on past earnings as a measure of the profitability of an investment, without adequately considering potential risks. Therefore, the development of professional risk management systems was often neglected. Thus, the possibility of high losses was not appropriately incorporated in their investment strategies. The consequences of such mistreatment became evident in the mid 1990s, when some of the world’s largest companies faced huge losses and sometimes even insolvency. Most of these failures were a direct result of inappropriate use of financial instruments and insufficient internal control mechanisms. The most spectacular debacles even resulted in losses of more than one billion dollars for each affected institution. In case of Barings Bank, a single trader ruined the 233-year old British financial institution by inappropriate investments in high-risk futures in 1995. The consequent loss of $1.3 billion, realized in a very short period, could not be absorbed and forced the downfall of Barings. At Daiwa Bank, it was also a single trader who caused a $1.1 billion deficit. In contrast, the losses were accumulated over 11 years from 1984. Another well-publicized bankruptcy was declared in 1994 by the Californian Orange County, after losses of $1.8 billion. Such evidence of poor risk management and control shows that proper financial risk management is crucial for all kinds of institutions in order to guarantee stability and continuity. Therefore, it is necessary to establish adequate risk management processes and to develop appropriate tools, which quantify risk exposures of both entire institutions and single financial instruments. This risk quantification should alert management early enough to prevent exceptional losses. One of the key concepts addressing these prob-lems of modern risk management was introduced in 1993 with the Value-at-Risk (VaR) models.
Addresses newly exposed weaknesses of financial risk models in the context of market stress scenarios This will be the definitive book for readers looking to improve their approach to modeling financial risk
Written by leading market risk academic, Professor Carol Alexander, Value-at-Risk Models forms part four of the Market Risk Analysis four volume set. Building on the three previous volumes this book provides by far the most comprehensive, rigorous and detailed treatment of market VaR models. It rests on the basic knowledge of financial mathematics and statistics gained from Volume I, of factor models, principal component analysis, statistical models of volatility and correlation and copulas from Volume II and, from Volume III, knowledge of pricing and hedging financial instruments and of mapping portfolios of similar instruments to risk factors. A unifying characteristic of the series is the pedagogical approach to practical examples that are relevant to market risk analysis in practice. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include: Parametric linear value at risk (VaR)models: normal, Student t and normal mixture and their expected tail loss (ETL); New formulae for VaR based on autocorrelated returns; Historical simulation VaR models: how to scale historical VaR and volatility adjusted historical VaR; Monte Carlo simulation VaR models based on multivariate normal and Student t distributions, and based on copulas; Examples and case studies of numerous applications to interest rate sensitive, equity, commodity and international portfolios; Decomposition of systematic VaR of large portfolios into standard alone and marginal VaR components; Backtesting and the assessment of risk model risk; Hypothetical factor push and historical stress tests, and stress testing based on VaR and ETL.
Implementing Value at Risk Philip Best Value at Risk (VAR) is an estimate of the potential loss on a trading or investment portfolio. Its use has swept the banking world and is now accepted as an essential tool in any risk manager's briefcase. Perhaps the greatest strength of VAR is that it can cope with virtually all financial products, from simple securities through to complex exotic derivatives. This allows the risk taken, across diverse trading activities, to be compared. This said, VAR is no panacea. It is as critical to understand when the use of VAR is inappropriate as it is to understand the value VAR can add to a bank's understanding and control of its risks. This book aims to explain how VAR can be used as an integral part of a risk and business management framework, rather than as a stand-alone tool. The objectives of this book are to explain: What VAR is - and isn't! How to calculate VAR - the three main methods Why stress testing is needed to complement VAR How to make stress testing effective How to use VAR and stress testing to manage risk How to use VAR to improve a bank's performance VAR as a regulatory measure of risk and capital Risk management practitioners, general bank managers, consultants and students of finance and risk management will find this book, and the software package included, an invaluable addition to their library. Finance/Investment
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.