Download Free Refining Value At Risk Estimates Book in PDF and EPUB Free Download. You can read online Refining Value At Risk Estimates and write the review.

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
Value at Risk (VAR) is rapidly emerging as the dominant methodology for estimating precisely how much money is at risk each day in the financial markets. This book provides an objective view of VAR, analyzing its pitfalls and benefits.
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.
This book focuses on structural changes and economic modeling. It presents papers describing how to model structural changes, as well as those introducing improvements to the existing before-structural-changes models, making it easier to later on combine these models with techniques describing structural changes. The book also includes related theoretical developments and practical applications of the resulting techniques to economic problems. Most traditional mathematical models of economic processes describe how the corresponding quantities change with time. However, in addition to such relatively smooth numerical changes, economical phenomena often undergo more drastic structural change. Describing such structural changes is not easy, but it is vital if we want to have a more adequate description of economic phenomena – and thus, more accurate and more reliable predictions and a better understanding on how best to influence the economic situation.
The most up-to-date resource on market risk methodologies Financial professionals in both the front and back office require an understanding of market risk and how to manage it. Measuring Market Risk provides this understanding with an overview of the most recent innovations in Value at Risk (VaR) and Expected Tail Loss (ETL) estimation. This book is filled with clear and accessible explanations of complex issues that arise in risk measuring-from parametric versus nonparametric estimation to incre-mental and component risks. Measuring Market Risk also includes accompanying software written in Matlab—allowing the reader to simulate and run the examples in the book.
Value-at-Risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a new simple approach to estimation of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting the standard normal distribution with time-varying higher moments. We allow the first four moments of the GCE to depend on past information, which leads to a more accurate approximation of the tails of the distribution. The results unambiguously show that our GCE-based VaR forecasts provide accurate and robust estimates of the realised VaR, outperforming those generated by the constant-higher-moments models.
The classification, measurement, and management of risk are central problems in the investment process. Over the past 25 years, Value at Risk (VaR) became the common universal standard in risk measurement. However, the financial crisis of 2007/2009 clearly demonstrated great discrepancies in risk estimates based on this indicator. In this report, three of the field’s leading experts objectively consider each key criticism of VaR in recent professional literature, including VaR’s underestimation of the magnitude and frequency of extreme outcomes, the difficulty of obtaining reliable VaR estimates for complex portfolios, the limited value of historical data, imperfections in the effective market hypothesis that underlies VaR, and several more. Next, the authors carefully review refinements and alternatives that have been proposed as potential replacements or complements, including Conditional VaR (Expected Shortfall), Shock VaR, modifications in the handling of parameters uncertainty, liquidity adjustment, higher moments, and more. They conclude by discussing why a sound risk management system continues to require deep understanding of complex adaptive and often irrational market mechanisms and still cannot be reduced to a mere combination of indicators, no matter how sophisticated they are.