ARCH Models for Financial Applications.
Stavros. Degiannakis
Bok Engelsk 2010 · Electronic books.
| Omfang | 1 online resource (560 pages)
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|---|---|
| Utgave | 1st ed.
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| Opplysninger | ARCH Models for Financial Applications -- Contents -- Preface -- Notation -- 1 What is an ARCH process? -- 1.1 Introduction -- 1.2 The autoregressive conditionally heteroscedastic process -- 1.3 The leverage effect -- 1.4 The non-trading period effect -- 1.5 The non-synchronous trading effect -- 1.6 The relationship between conditional variance and conditional mean -- 1.6.1 The ARCH in mean model -- 1.6.2 Volatility and serial correlation -- 2 ARCH volatility specifications -- 2.1 Model specifications -- 2.2 Methods of estimation -- 2.2.1 Maximum likelihood estimation -- 2.2.2 Numerical estimation algorithms -- 2.2.3 Quasi-maximum likelihood estimation -- 2.2.4 Other estimation methods -- 2.3 Estimating the GARCH model with EViews 6: an empirical example -- 2.4 Asymmetric conditional volatility specifications -- 2.5 Simulating ARCH models using EViews -- 2.6 Estimating asymmetric ARCH models with G@RCH 4.2 OxMetrics: an empirical example -- 2.7 Misspecification tests -- 2.7.1 The Box-Pierce and Ljung-Box Q statistics -- 2.7.2 Tse's residual based diagnostic test for conditional heteroscedasticity -- 2.7.3 Engle's Lagrange multiplier test -- 2.7.4 Engle and Ng's sign bias tests -- 2.7.5 The Breusch-Pagan, Godfrey, Glejser, Harvey and White tests -- 2.7.6 The Wald, likelihood ratio and Lagrange multiplier tests -- 2.8 Other ARCH volatility specifications -- 2.8.1 Regime-switching ARCH models -- 2.8.2 Extended ARCH models -- 2.9 Other methods of volatility modelling -- 2.10 Interpretation of the ARCH process -- Appendix -- 3 Fractionally integrated ARCH models -- 3.1 Fractionally integrated ARCH model specifications -- 3.2 Estimating fractionally integrated ARCH models using G@RCH 4.2 OxMetrics: an empirical example -- 3.3 A more detailed investigation of the normality of the standardized residuals: goodness-of-fit tests -- 3.3.1 EDF tests.. - 11.1.2 Asymmetric and long-memory model specifications -- 11.2 Maximum likelihood estimation -- 11.3 Estimating multivariate ARCH models using EViews 6 -- 11.4 Estimating multivariate ARCH models using G@RCH 5.0 -- 11.5 Evaluation of multivariate ARCH models -- Appendix -- References -- Author Index -- Subject Index.. - 3.3.2 Chi-square tests -- 3.3.3 QQ plots -- 3.3.4 Goodness-of-fit tests using EViews and G@RCH -- Appendix -- 4 Volatility forecasting: an empirical example using EViews 6 -- 4.1 One-step-ahead volatility forecasting -- 4.2 Ten-step-ahead volatility forecasting -- Appendix -- 5 Other distributional assumptions -- 5.1 Non-normally distributed standardized innovations -- 5.2 Estimating ARCH models with non-normally distributed standardized innovations using G@RCH 4.2 OxMetrics: an empirical example -- 5.3 Estimating ARCH models with non-normally distributed standardized innovations using EViews 6: an empirical example -- 5.4 Estimating ARCH models with non-normally distributed standardized innovations using EViews 6: the logl object -- Appendix -- 6 Volatility forecasting: an empirical example using G@RCH Ox -- Appendix -- 7 Intraday realized volatility models -- 7.1 Realized volatility -- 7.2 Intraday volatility models -- 7.3 Intraday realized volatility andARFIMAXmodels in G@RCH 4.2 OxMetrics: an empirical example -- 7.3.1 Descriptive statistics -- 7.3.2 In-sample analysis -- 7.3.3 Out-of-sample analysis -- 8 Applications in value-at-risk, expected shortfall and options pricing -- 8.1 One-day-ahead value-at-risk forecasting -- 8.1.1 Value-at-risk -- 8.1.2 Parametric value-at-risk modelling -- 8.1.3 Intraday data and value-at-risk modelling -- 8.1.4 Non-parametric and semi-parametric value-at-risk modelling -- 8.1.5 Back-testing value-at-risk -- 8.1.6 Value-at-risk loss functions -- 8.2 One-day-ahead expected shortfall forecasting -- 8.2.1 Historical simulation and filtered historical simulation for expected shortfall -- 8.2.2 Loss functions for expected shortfall -- 8.3 FTSE100 index: one-step-ahead value-at-risk and expected shortfall forecasting -- 8.4 Multi-period value-at-risk and expected shortfall forecasting.. - 8.5 ARCH volatility forecasts in Black-Scholes option pricing -- 8.5.1 Options -- 8.5.2 Assessing the performance of volatility forecasting methods -- 8.5.3 Black-Scholes option pricing using a set of ARCH processes -- 8.5.4 Trading straddles based on a set of ARCH processes -- 8.5.5 Discussion -- 8.6 ARCH option pricing formulas -- 8.6.1 Computation of Duan's ARCH option prices: an example -- Appendix -- 9 Implied volatility indices and ARCH models -- 9.1 Implied volatility -- 9.2 The VIX index -- 9.3 The implied volatility index as an explanatory variable -- 9.4 ARFIMAX model for implied volatility index -- Appendix -- 10 ARCH model evaluation and selection -- 10.1 Evaluation of ARCH models -- 10.1.1 Model evaluation viewed in terms of information criteria -- 10.1.2 Model evaluation viewed in terms of statistical loss functions -- 10.1.3 Consistent ranking -- 10.1.4 Simulation, estimation and evaluation -- 10.1.5 Point, interval and density forecasts -- 10.1.6 Model evaluation viewed in terms of loss functions based on the use of volatility forecasts -- 10.2 Selection of ARCH models -- 10.2.1 The Diebold-Mariano test -- 10.2.2 The Harvey-Leybourne-Newbold test -- 10.2.3 The Morgan-Granger-Newbold test -- 10.2.4 White's reality check for data snooping -- 10.2.5 Hansen's superior predictive ability test -- 10.2.6 The standardized prediction error criterion -- 10.2.7 Forecast encompassing tests -- 10.3 Application of loss functions as methods of model selection -- 10.3.1 Applying the SPEC model selection method -- 10.3.2 Applying loss functions as methods of model selection -- 10.3.3 Median values of loss functions as methods of model selection -- 10.4 The SPA test for VaR and expected shortfall -- Appendix -- 11 Multivariate ARCH models -- 11.1 Model specifications -- 11.1.1 Symmetric model specifications.. - "Numerous articles on the Autoregressive Conditional Heteroskedastic (ARCH) process, an increasingly popular financial modeling technique, exist in various international journals. Now Xekalaki and Degiannakis (both statistics, Athens U. of Economics and Business, Greece) provide a thorough treatment of the ARCH theory and its practical applications, in a textbook for postgraduate and final-year undergraduate students which could serve as reference work for academics and financial market professionals." (Book News Inc, November 2010).
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| ISBN | 9780470688021
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