Beschreibung Market Risk Analysis: Volume IV: Value at Risk Models (Wiley Finance Series, Band 4). 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.
Market Risk Analysis: Volume IV: Value at Risk Models ~ Market Risk Analysis: Volume IV: Value at Risk Models (Wiley Finance Series, Band 4) / Alexander, Carol / ISBN: 9780470997888 / Kostenloser Versand fĂŒr alle BĂŒcher mit Versand und Verkauf duch .
Value At Risk (VAR) Models - MIT OpenCourseWare ~ Lecture 7: Value At Risk (VAR) Models Ken Abbott Developed for educational use at MIT and for publication through MIT OpenCourseware. No investment decisions should be made in reliance on this material. 2 Risk Management's Mission . 1. To ensure that management is fully informed about the risk profile of the bank. 2. To protect the bank against unacceptably large losses resulting from .
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VALUE AT RISK (VAR) ~ possible loss in value from ânormal market riskâ as opposed to all risk, requiring that we draw distinctions between normal and abnormal risk as well as between market and non-market risk. While Value at Risk can be used by any entity to measure its risk exposure, it is used most often by commercial and investment banks to capture the potential loss in value of their traded portfolios from .
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Basel Committee on Banking Supervision ~ Overview of the revised internal models approach for market risk An illustration of the process and policy design of the internal models-based approaches (IMA) is set out in the diagram below.
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An Introduction to Value at Risk (VAR) ~ Value at Risk (VAR) calculates the maximum loss expected (or worst case scenario) on an investment, over a given time period and given a specified degree of confidence. We looked at three methods .
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Market Risk Analysis Quantitative Methods In Finance Volume I ~ market risk analysis quantitative methods in finance volume i Sep 13, 2020 Posted By Roald Dahl Library TEXT ID 46127308 Online PDF Ebook Epub Library research in financial markets volume 1 issue 1 to volume 12 issue 4 qualitative research in financial markets available volumes and issues books and journals case studies