ARCH Models for Financial Applications provides background on the theory of ARCH models, with a focus on practical implementation via applications to real data and examples worked with econometrics packages. The interactional exposition of the ARCH theory, and its implementation in practice that the authors adopt, helps readers get a deeper understanding of the models and their use as tools in applied financial contexts. Intended for readers seeking an aptitude in the applications of financial econometric modeling, this book requires only a basic knowledge of econometrics and basic undergraduate-level statistics.
Prologue. Notation. 1 What is an ARCH process? 1.1 Introduction. 1.2 The Autoregressive ConditionallyHeteroskedastic Process. 1.3 The Leverage Effect. 1.4 The Non-trading Period Effect. 1.5 Non-synchronous Trading Effect. 1.6 The Relationship between Conditional Variance andConditional Mean. 2 ARCH Volatility Specifications. 2.1 Model Specifications. 2.2 Methods of Estimation. 2.3. Estimating the GARCH Model with EViews 6: An EmpiricalExample. 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. 2.8 Other ARCH Volatility Specifications. 2.9 Other Methods of Volatility Modeling. 2.10 Interpretation of the ARCH Process. 3 Fractionally Integrated ARCH Models. 3.1 Fractionally Integrated ARCH Model Specifications. 3.2 Estimating Fractionally Integrated ARCH Models Using G@RCH4.2 OxMetrics An Empirical Example. 3.3 A More Detailed Investigation of the Normality of theStandardized Residuals Goodness-of-fit Tests. 4 Volatility Forecasting: An Empirical Example Using EViews6. 4.1 One-step-ahead Volatility Forecasting. 4.2 Ten-step-ahead Volatility Forecasting. 5 Other Distributional Assumptions. 5.1 Non-Normally Distributed Standardized Innovations. 5.2 Estimating ARCH Models with Non-Normally DistributedStandardized Innovations Using G@RCH 4.2 OxMetrics AnEmpirical Example. 5.3 Estimating ARCH Models with Non-Normally DistributedStandardized Innovations Using EViews 6 An EmpiricalExample. 5.4 Estimating ARCH Models with Non-Normally DistributedStandardized Innovations Using EViews 6 The LogLObject. 6 Volatility Forecasting: An Empirical Example Using G@RCHOx. 7 Intra-Day Realized Volatility Models. 7.1 Realized Volatility. 7.2 Intra-Day Volatility Models. 7.3 Intra-Day Realized Volatility & ARFIMAX Models in G@RCH4.2 OxMetrics An Empirical example. 8 Applications in Value-at-Risk, Expected Shortfalls, OptionsPricing. 8.1 One-day-ahead Value-at-Risk Forecasting. 8.2 One-day-ahead Expected Shortfalls Forecasting. 8.3 FTSE100 Index: One-step-ahead Value-at-Risk and ExpectedShortfall Forecasting. 8.4 Multi-period Value-at-Risk and Expected ShortfallsForecasting. 8.5 ARCH Volatility Forecasts in Black and Scholes OptionPricing. 8.6 ARCH Option Pricing Formulas. 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 Modeling for Implied Volatility Index. 10 ARCH Model Evaluation and Selection. 10.1 Evaluation of ARCH Models. 10.2 Selection of ARCH Models. 10.3 Application of Loss Functions as Methods of ModelSelection. 10.4 The SPA Test for VaR and Expected Shortfalls. 11 Multivariate ARCH Models. 11.1 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. References. Author Index. Subject Index.