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Robust Backtesting Tests for Value-at-Risk Models

Robust Backtesting Tests for Value-at-Risk Models
Author: Juan Carlos Escanciano
Publisher:
Total Pages: 0
Release: 2009
Genre:
ISBN:
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Backtesting methods are statistical tests designed to uncover excessive risk-taking from financial institutions. We show in this paper that these methods are subject to the presence of model risk produced by a wrong specification of the conditional VaR model, and derive its effect on the asymptotic distribution of the relevant out-of-sample tests. We also show that in the absence of estimation risk, the unconditional backtest is affected by model misspecification but the independence test is not. Our solution for the general case consists on proposing robust subsampling techniques to approximate the true sampling distribution of these tests. We carry out a Monte Carlo study to see the importance of these effects in finite samples for location-scale models that are wrongly specified but correct on average. An application to Dow-Jones Index shows the impact of correcting for model risk on backtesting procedures for different dynamic VaR models measuring risk exposure.

A Comparison Between Advanced Value at Risk Models and Their Backtesting in Different Portfolios

A Comparison Between Advanced Value at Risk Models and Their Backtesting in Different Portfolios
Author: Christian Steinlechner
Publisher:
Total Pages: 92
Release: 2013-08
Genre:
ISBN: 9783656319009
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Master's Thesis from the year 2012 in the subject Business economics - Miscellaneous, grade: 1, Fachhochschule des bfi Wien GmbH, course: Riskmanagement, language: English, comment: gewann den 2. Platz beim CFA Austria Prize, abstract: This thesis analyses three VaR models in detail. To begin with, there is a short description of the theoretical background of the models. Next, four different backtests are performed on two different portfolios for each of the three models. The source code used for the implementation is available in the appendix. The main part will deal with the interpretation of the backtesting results. Each model will be compared with the same backtests and dimensionality, which allows the comparison of models with each other. The main outcome of this backtest is the knowledge as to how a model should be calibrated and how robust a model is. In a validation procedure, the author selects that calibration which yields the best results for each model.

Market Risk Analysis, Value at Risk Models

Market Risk Analysis, Value at Risk Models
Author: Carol Alexander
Publisher: John Wiley & Sons
Total Pages: 503
Release: 2009-02-09
Genre: Business & Economics
ISBN: 0470997885
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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.

Backtesting Value at Risk and Expected Shortfall

Backtesting Value at Risk and Expected Shortfall
Author: Simona Roccioletti
Publisher: Springer
Total Pages: 155
Release: 2015-12-04
Genre: Business & Economics
ISBN: 365811908X
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In this book Simona Roccioletti reviews several valuable studies about risk measures and their properties; in particular she studies the new (and heavily discussed) property of "Elicitability" of a risk measure. More important, she investigates the issue related to the backtesting of Expected Shortfall. The main contribution of the work is the application of "Test 1" and "Test 2" developed by Acerbi and Szekely (2014) on different models and for five global market indexes.

Implementing Value at Risk

Implementing Value at Risk
Author: Philip Best
Publisher: John Wiley & Sons
Total Pages: 224
Release: 2000-11-21
Genre: Business & Economics
ISBN: 0470865962
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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

Elements of Financial Risk Management

Elements of Financial Risk Management
Author: Peter Christoffersen
Publisher: Academic Press
Total Pages: 346
Release: 2011-11-22
Genre: Business & Economics
ISBN: 0123744482
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The Second Edition of this best-selling book expands its advanced approach to financial risk models by covering market, credit, and integrated risk. With new data that cover the recent financial crisis, it combines Excel-based empirical exercises at the end of each chapter with online exercises so readers can use their own data. Its unified GARCH modeling approach, empirically sophisticated and relevant yet easy to implement, sets this book apart from others. Five new chapters and updated end-of-chapter questions and exercises, as well as Excel-solutions manual, support its step-by-step approach to choosing tools and solving problems. Examines market risk, credit risk, and operational risk Provides exceptional coverage of GARCH models Features online Excel-based empirical exercises

Measuring Market Risk with Value at Risk

Measuring Market Risk with Value at Risk
Author: Pietro Penza
Publisher: John Wiley & Sons
Total Pages: 324
Release: 2001
Genre: Business & Economics
ISBN: 9780471393139
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"This book, Measuring Market Risk with Value at Risk by Vipul Bansal and Pietro Penza, has three advantages over earlier works on the subject. First, it takes a decidedly global approach-an essential ingredient for any comprehensive work on market risk. Second, it ties the scientifically grounded, yet intuitively appealing, VaR measure to earlier, more idiosyncratic measures of market risk that are used in specific market environs (e.g., duration in fixed income). Finally, it encompasses all of the accepted approaches to calculating a VaR measure and presents them in a clearly explained fashion with supporting illustrations and completely worked-out examples." -from the Foreword by John F. Marshall, PhD, Principal, Marshall, Tucker & Associates, LLC "Measuring Market Risk with Value at Risk offers a much-needed intellectual bridge, a translation from the esoteric realm of mathematical finance to the domain of financial managers who seek guidance in applying developments from this important field of research as well as that of MBA-level graduate instruction. I believe the authors have done a commendable job of providing a carefully crafted, highly readable, and most useful work, and intend to recommend it to all those involved in business risk management applications." -Anthony F. Herbst, PhD, Professor of Finance and C.R. and D.S. Carter Chair, The University of Texas, El Paso and Founding editor of The Journal of Financial Engineering (1991-1998) "Finally there's a book that strikes a balance between rigor and application in the area of risk management in the banking industry. This innovative book is a MUST for both novices and professionals alike." -Robert P. Yuyuenyongwatana, PhD, Associate Professor of Finance, Cameron University "Measuring Market Risk with Value at Risk is one of the most complete discussions of this emerging topic in finance that I have seen. The authors develop a logical and rigorous framework for using VaR models, providing both historical references and analytical applications." -Kevin Wynne, PhD, Associate Professor of Finance, Lubin School of Business, Pace University

Finite Mixture Models

Finite Mixture Models
Author: Geoffrey McLachlan
Publisher: John Wiley & Sons
Total Pages: 419
Release: 2004-03-22
Genre: Mathematics
ISBN: 047165406X
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An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.

Backtesting Value-at-Risk

Backtesting Value-at-Risk
Author: Peter Christoffersen
Publisher:
Total Pages:
Release: 2010
Genre:
ISBN:
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Financial risk model evaluation or backtesting is a key part of the internal model`s approach to market risk management as laid out by the Basle Committee on Banking Supervision. However, existing backtesting methods have relatively low power in realistic small sample settings. Our contribution is the exploration of new tools for backtesting based on the duration of days between the violations of the Value-at-Risk. Our Monte Carlo results show that in realistic situations, the new duration-based tests have considerably better power properties than the previously suggested tests.