Financial risk forecasting: the theory and practice of forecasting market risk, with implementation in R and Matlab

Medientyp: E-Book

Titel: Financial risk forecasting : the theory and practice of forecasting market risk, with implementation in R and Matlab

Beteiligte: Daníelsson, Jón [Verfasser:in]

Erschienen: Chichester: John Wiley, 2011

Umfang: 1 Online-Ressource (xxi, 274 pages); illustrations

Sprache: Englisch

DOI: 10.1002/9781119205869

ISBN: 1119205867; 111997710X; 1119977118; 1119977126; 9781119977100; 9781119977117; 9781119205869; 9781119977124

Identifikator:

Schlagwörter: Financial risk management Simulation methods ; Financial risk management Forecasting ; Financial futures ; BUSINESS & ECONOMICS ; Insurance ; Risk Assessment & Management ; Finances - Gestion du risque - Méthodes de simulation ; Finances - Gestion du risque - Prévision ; Gestió financera ; Gestió del risc ; Previsió ; Mètodes de simulació ; Llibres electrònics

Entstehung:

Anmerkungen: Includes bibliographical references and index

Beschreibung: 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 pres

Cover; Dedication; Title page; Copyright; Preface; Acknowledgments; Abbreviations; Notation; 1 Financial markets, prices and risk; 1.1 Prices, returns and stock indices; 1.2 S & P 500 returns; 1.3 The stylized facts of financial returns; 1.4 Volatility; 1.5 Nonnormality and fat tails; 1.6 Identification of fat tails; 1.7 Nonlinear dependence; 1.8 Copulas; 1.9 Summary; 2 Univariate volatility modeling; 2.1 Modeling Volatility; 2.2 Simple volatility models; 2.3 GARCH and conditional volatility; 2.4 Maximum likelihood estimation of volatility models; 2.5 Diagnosing volatility models.

Machine generated contents note:1.Financial markets, prices and risk --1.1.Prices, returns and stock indices --1.1.1.Stock indices --1.1.2.Prices and returns --1.2.S&P 500 returns --1.2.1.S&P 500 statistics --1.2.2.S&P 500 statistics in R and Matlab --1.3.stylized facts of financial returns --1.4.Volatility --1.4.1.Volatility clusters --1.4.2.Volatility clusters and the ACF --1.5.Nonnormality and fat tails --1.6.Identification of fat tails --1.6.1.Statistical tests for fat tails --1.6.2.Graphical methods for fat tail analysis --1.6.3.Implications of fat tails in finance --1.7.Nonlinear dependence --1.7.1.Sample evidence of nonlinear dependence --1.7.2.Exceedance correlations --1.8.Copulas --1.8.1.Gaussian copula --1.8.2.theory of copulas --1.8.3.application of copulas --1.8.4.Some challenges in using copulas --1.9.Summary --2.Univariate volatility modeling --2.1.Modeling volatility --2.2.Simple volatility models --2.2.1.Moving average models --2.2.2.EWMA model --2.3.GARCH and conditional volatility --2.3.1.ARCH --2.3.2.GARCH --2.3.3."memory" of a GARCH model --2.3.4.Normal GARCH --2.3.5.Student-t GARCH --2.3.6.(G)ARCH in mean --2.4.Maximum likelihood estimation of volatility models --2.4.1.ARCH(1) likelihood function --2.4.2.GARCH(1, 1) likelihood function --2.4.3.On the importance of σ1 --2.4.4.Issues in estimation --2.5.Diagnosing volatility models --2.5.1.Likelihood ratio tests and parameter significance --2.5.2.Analysis of model residuals --2.5.3.Statistical goodness-of-fit measures --2.6.Application of ARCH and GARCH --2.6.1.Estimation results --2.6.2.Likelihood ratio tests --2.6.3.Residual analysis --2.6.4.Graphical analysis --2.6.5.Implementation --2.7.Other GARCH-type models --2.7.1.Leverage effects and asymmetry --2.7.2.Power models --2.7.3.APARCH --2.7.4.Application of APARCH models --2.7.5.Estimation of APARCH --2.8.Alternative volatility models --2.8.1.Implied volatility --2.8.2.Realized volatility --2.8.3.Stochastic volatility --2.9.Summary --3.Multivariate volatility models --3.1.Multivariate volatility forecasting --3.1.1.Application --3.2.EWMA --3.3.Orthogonal GARCH --3.3.1.Orthogonalizing covariance --3.3.2.Implementation --3.3.3.Large-scale implementations --3.4.CCC and DCC models --3.4.1.Constant conditional correlations (CCC) --3.4.2.Dynamic conditional correlations (DCC) --3.4.3.Implementation --3.5.Estimation comparison --3.6.Multivariate extensions of GARCH --3.6.1.Numerical problems --3.6.2.BEKK model --3.7.Summary --4.Risk measures --4.1.Defining and measuring risk --4.2.Volatility --4.3.Value-at-risk --4.3.1.Is VaR a negative or positive number--4.3.2.three steps in VaR calculations --4.3.3.Interpreting and analyzing VaR --4.3.4.VaR and normality --4.3.5.Sign of VaR --4.4.Issues in applying VaR --4.4.1.VaR is only a quantile --4.4.2.Coherence --4.4.3.Does VaR really violate subadditivity--4.4.4.Manipulating VaR --4.5.Expected shortfall --4.6.Holding periods, scaling and the square root of time --4.6.1.Length of holding periods --4.6.2.Square-root-of-time scaling --4.7.Summary --5.Implementing risk forecasts --5.1.Application --5.2.Historical simulation --5.2.1.Expected shortfall estimation --5.2.2.Importance of window size --5.3.Risk measures and parametric methods --5.3.1.Deriving VaR --5.3.2.VaR when returns are normally distributed --5.3.3.VaR under the Student-t distribution --5.3.4.Expected shortfall under normality --5.4.What about expected returns--5.5.VaR with time-dependent volatility --5.5.1.Moving average --5.5.2.EWMA --5.5.3.GARCH normal --5.5.4.Other GARCH models --5.6.Summary --6.Analytical value-at-risk for options and bonds --6.1.Bonds --6.1.1.Duration-normal VaR --6.1.2.Accuracy of duration-normal VaR --6.1.3.Convexity and VaR --6.2.Options --6.2.1.Implementation --6.2.2.Delta-normal VaR --6.2.3.Delta and gamma --6.3.Summary --7.Simulation methods for VaR for options and bonds --7.1.Pseudo random number generators --7.1.1.Linear congruental generators --7.1.2.Nonuniform RNGs and transformation methods --7.2.Simulation pricing --7.2.1.Bonds --7.2.2.Options --7.3.Simulation of VaR for one asset --7.3.1.Monte Carlo VaR with one basic asset --7.3.2.VaR of an option on a basic asset --7.3.3.Options and a stock --7.4.Simulation of portfolio VaR --7.4.1.Simulation of portfolio VaR for basic assets --7.4.2.Portfolio VaR for options --7.4.3.Richer versions --7.5.Issues in simulation estimation --7.5.1.quality of the RNG --7.5.2.Number of simulations --7.6.Summary --8.Backtesting and stress testing --8.1.Backtesting --8.1.1.Market risk regulations --8.1.2.Estimation window length --8.1.3.Testing window length --8.1.4.Violation ratios --8.2.Backtesting the S&P 500 --8.2.1.Analysis --8.3.Significance of backtests --8.3.1.Bernoulli coverage test --8.3.2.Testing the independence of violations --8.3.3.Testing VaR for the S&P 500 --8.3.4.Joint test --8.3.5.Loss-function-based backtests --8.4.Expected shortfall backtesting --8.5.Problems with backtesting --8.6.Stress testing --8.6.1.Scenario analysis --8.6.2.Issues in scenario analysis --8.6.3.Scenario analysis and risk models --8.7.Summary --9.Extreme value theory --9.1.Extreme value theory --9.1.1.Types of tails --9.1.2.Generalized extreme value distribution --9.2.Asset returns and fat tails --9.3.Applying EVT --9.3.1.Generalized Pareto distribution --9.3.2.Hill method --9.3.3.Finding the threshold --9.3.4.Application to the S&P 500 index --9.4.Aggregation and convolution --9.5.Time dependence --9.5.1.Extremal index --9.5.2.Dependence in ARCH --9.5.3.When does dependence matter--9.6.Summary --10.Endogenous risk --10.1.Millennium Bridge --10.2.Implications for financial risk management --10.2.1.2007-2010 crisis --10.3.Endogenous market prices --10.4.Dual role of prices --10.4.1.Dynamic trading strategies --10.4.2.Delta hedging --10.4.3.Simulation of feedback --10.4.4.Endogenous risk and the 1987 crash --10.5.Summary --APPENDICES --A.Financial time series --A.1.Random variables and probability density functions --A.1.1.Distributions and densities --A.1.2.Quantiles --A.1.3.normal distribution --A.1.4.Joint distributions --A.1.5.Multivariate normal distribution --A.1.6.Conditional distribution --A.1.7.Independence --A.2.Expectations and variance --A.2.1.Properties of expectation and variance --A.2.2.Covariance and independence --A.3.Higher order moments --A.3.1.Skewness and kurtosis --A.4.Examples of distributions --A.4.1.Chi-squared (Χ2) --A.4.2.Student-t --A.4.3.Bernoulli and binomial distributions --A.5.Basic time series concepts --A.5.1.Autocovariances and autocorrelations --A.5.2.Stationarity --A.5.3.White noise --A.6.Simple time series models --A.6.1.moving average model --A.6.2.autoregressive model --A.6.3.ARMA model --A.6.4.Random walk --A.7.Statistical hypothesis testing --A.7.1.Central limit theorem --A.7.2.p-values --A.7.3.Type 1 and type 2 errors and the power of the test --A.7.4.Testing for normality --A.7.5.Graphical methods: QQ plots --A.7.6.Testing for autocorrelation --A.7.7.Engle LM test for volatility clusters.

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