ARCH Models for Financial Applications [精装]

Preface p. xi
Notation p. xv
What is an ARCH process? p. 1
Introduction p. 1
The autoregressive conditionally heteroscedastic process p. 8
The leverage effect p. 13
The non-trading period effect p. 15
The non-synchronous trading effect p. 15
The relationship between conditional variance and conditional mean p. 16
The ARCH in mean model p. 16
Volatility and serial correlation p. 18
ARCH volatility specifications p. 19
Model specifications p. 19
Methods of estimation p. 23
Maximum likelihood estimation p. 23
Numerical estimation algorithms p. 25
Quasi-maximum likelihood estimation p. 28
Other estimation methods p. 29
Estimating the GARCH model with EViews 6: an empirical example p. 31
Asymmetric conditional volatility specifications p. 42
Simulating ARCH models using EViews p. 49
Estimating asymmetric ARCH models with G@RCH 4.2 OxMetrics: an empirical example p. 55
Misspecification tests p. 66
The Box-Pierce and Ljung-Box Q statistics p. 66
Tse's residual based diagnostic test for conditional heteroscedasticity p. 67
Engle's Lagrange multiplier test p. 67
Engle and Ng's sign bias tests p. 68
The Breusch-Pagan, Godfrey, Glejser, Harvey and White tests p. 69
The Wald, likelihood ratio and Lagrange multiplier tests p. 69
Other ARCH volatility specifications p. 70
Regime-switching ARCH models p. 70
Extended ARCH models p. 72
Other methods of volatility modelling p. 76
Interpretation of the ARCH process p. 82
Appendix p. 86
Fractionally integrated ARCH models p. 107
Fractionally integrated ARCH model specifications p. 107
Estimating fractionally integrated ARCH models using G@RCH 4.2 OxMetrics: an empirical example p. 111
A more detailed investigation of the normality of the standardized residuals: goodness-of-fit tests p. 122
EDF tests p. 123
Chi-square tests p. 124
QQ plots p. 125
Goodness-of-fit tests using EViews and G@RCH p. 126
Appendix p. 129
Volatility forecasting: an empirical example using EViews 6 p. 143
One-step-ahead volatility forecasting p. 143
Ten-step-ahead volatility forecasting p. 150
Appendix p. 154
Other distributional assumptions p. 163
Non-normally distributed standardized innovations p. 163
Estimating ARCH models with non-normally distributed standardized innovations using G@RCH 4.2 OxMetrics: an empirical example p. 168
Estimating ARCH models with non-normally distributed standardized innovations using EViews 6: an empirical example p. 174
Estimating ARCH models with non-normally distributed standardized innovations using EViews 6: the logl object p. 176
Appendix p. 182
Volatility forecasting: an empirical example using G@RCH Ox p. 185
Appendix p. 195
Intraday realized volatility models p. 217
Realized volatility p. 217
Intraday volatility models p. 220
Intraday realized volatility and ARFIMAX models in G@RCH 4.2 OxMetrics: an empirical example p. 223
Descriptive statistics p. 223
In-sample analysis p. 228
Out-of-sample analysis p. 232
Applications in value-at-risk, expected shortfall and options pricing p. 239
One-day-ahead value-at-risk forecasting p. 239
Value-at-risk p. 239
Parametric value-at-risk modelling p. 240
Intraday data and value-at-risk modelling p. 242
Non-parametric and semi-parametric value-at-risk modelling p. 244
Back-testing value-at-risk p. 245
Value-at-risk loss functions p. 248
One-day-ahead expected shortfall forecasting p. 248
Historical simulation and filtered historical simulation for expected shortfall p. 251
Loss functions for expected shortfall p. 251
FTSE100 index: one-step-ahead value-at-risk and expected shortfall forecasting p. 252
Multi-period value-at-risk and expected shortfall forecasting p. 258
ARCH volatility forecasts in Black-Scholes option pricing p. 260
Options p. 261
Assessing the performance of volatility forecasting methods p. 269
Black-Scholes option pricing using a set of ARCH processes p. 270
Trading straddles based on a set of ARCH processes p. 271
Discussion p. 279
ARCH option pricing formulas p. 281
Computaion of Duan's ARCH option prices: an example p. 286
Appendix p. 288
Implied volatility indices and ARCH models p. 341
Implied volatility p. 341
The VIX index p. 342
The implied volatility index as an explanatory variable p. 344
ARFIMAX model for implied volatility index p. 349
Appendix p. 352
ARCH model evaluation and selection p. 357
Evaluation of ARCH models p. 358
Model evaluation viewed in terms of information criteria p. 359
Model evaluation viewed in terms of statistical loss functions p. 360
Consistent ranking p. 367
Simulation, estimation and evaluation p. 377
Point, interval and density forecasts p. 383
Model evaluation viewed in terms of loss functions based on the use of volatility forecasts p. 384
Selection of ARCH models p. 386
The Diebold-Mariano test p. 386
The Harvey-Leybourne-Newbold test p. 389
The Morgan-Granger-Newbold test p. 389
White's reality check for data snooping p. 390
Hansen's superior predictive ability test p. 390
The standardized prediction error criterion p. 393
Forecast encompassing tests p. 400
Application of loss functions as methods of model selection p. 401
Applying the SPEC model selection method p. 401
Applying loss functions as methods of model selection p. 402
Median values of loss functions as methods of model selection p. 407
The SPA test for VaR and expected shortfall p. 408
Appendix p. 410
Multivariate ARCH models p. 445
Model Specifications p. 446
Symmetric model specifications p. 446
Asymmetric and long-memory model specifications p. 453
Maximum likelihood estimation p. 454
Estimating multivariate ARCH models using EViews 6 p. 456
Estimating multivariate ARCH models using G@RCH 5.0 p. 465
Evaluation of multivariate ARCH models p. 473
Appendix p. 475
References p. 479
Author Index p. 521
Subject Index p. 533