ARCH Models for Financial Applications

Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection.
Key Features:Presents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique.Assumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value–at–risk, option pricing and model evaluation.Uses empirical examples to demonstrate how the recent developments in ARCH can be implemented.Provides step–by–step instructive examples, using econometric software, such as Econometric Views and the G@RCH module for the Ox software package, used in Estimating and Forecasting ARCH Models.Accompanied by a CD–ROM containing links to the software as well as the datasets used in the examples.
Aimed at readers wishing to gain an aptitude in the applications of financial econometric modelling with a focus on practical implementation, via applications to real data and via examples worked with econometrics packages.

Spis treści:
Prologue
Symbols and Operators
Chapter 1: What is an ARCH process?
1.1 Introduction
1.2 The Autoregressive Conditionally Heteroskedastic (ARCH) 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 Varianc e and Conditional Mean
Chapter 2: ARCH Volatility Specifications
2.1 Model Specifications
2.2 Methods of Estimation
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
2.8 Other ARCH Volatility Specifications
2.9 Other Methods of Volatility Modeling
2.10 Interpretation of the ARCH Process
Chapter 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
Chapter 4: Volatility Forecasting: An Empirical Example Using EViews 6
4.1 One–step–ahead Volatility Forecasting
4.2 Ten–step–ahead Volatility Forecasting
Chapter 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
Chapter 6: Volatility Forecasting: An Empirical Example Using G@RCH Ox
6.1 One–step–ahead Volatility Forecasting
Chapter 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@RCH 4.2 OxMetrics – An Empirical example
Chapter 8: Applications in Value–at–Risk, Expected Shortfalls, Options Pricing
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 Expected Shortfall Forecasting
8.4 Multi–period Value–at–Risk and Expected Shortfalls Forecasting
8.5 ARCH Volatility Forecasts in Black and Scholes Option Pricing
8.6 ARCH Option Pricing Formulas
Chapter 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
Chapter 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 Model Selection.
10.4 The SPA Test for VaR and Expected Shortfalls
Chapter 11: Multivariate ARCH Models
11.1 Model Specifications
11.2 Maximum Likelihood Estimation
11.3 Estimating Multivariate ARCH Models Using EViews 6.0.
11.4 Estimating Multivariate ARCH Models Using G@RCH 5.0.
11.5 Evaluation of Multivariate ARCH Models.
References
Appendices
Author Index
Subject Index


Okładka tylna:
Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empiric al background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection.
Key Features:Presents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique.Assumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value–at–risk, option pricing and model evaluation.Uses empirical examples to demonstrate how the recent developments in ARCH can be implemented.Provides step–by–step instructive examples, using econometric software, such as Econometric Views and the G@RCH module for the Ox software package, used in Estimating and Forecasting ARCH Models.Accompanied by a CD–ROM containing links to the software as well as the datasets used in the examples.
Aimed at readers wishing to gain an aptitude in the applications of financial econometric modelling with a focus on practical implementation, via applications to real data and via examples worked with econometrics packages.