Xekalaki E., Degiannakis S. ARCH Models for Financial Applications

Wiley – 2010, 550 pages
ISBN 9780470066300


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.
Contents:
Prologue
Notation
What is an ARCH process?
Introduction
The Autoregressive Conditionally Heteroskedastic Process
The Leverage Effect
The Non-trading Period Effect
Non-synchronous Trading Effect
The Relationship between Conditional Variance and Conditional Mean
ARCH Volatility Specifications
Model Specifications
Methods of Estimation
. Estimating the GARCH Model with EViews : An Empirical Example.
. Asymmetric Conditional Volatility Specifications
. Simulating ARCH Models Using EViews
. Estimating Asymmetric ARCH Models with GARCH OxMetrics – An Empirical Example.
. Misspecification Tests
Other ARCH Volatility Specifications
Other Methods of Volatility Modeling
Interpretation of the ARCH Process
Fractionally Integrated ARCH Models
Fractionally Integrated ARCH Model Specifications
Estimating Fractionally Integrated ARCH Models Using GARCH OxMetrics – An Empirical Example
A More Detailed Investigation of the Normality of the Standardized Residuals – Goodness-of-fit Tests
Volatility Forecasting: An Empirical Example Using EViews
One-step-ahead Volatility Forecasting
Ten-step-ahead Volatility Forecasting
Other Distributional Assumptions
Non-Normally Distributed Standardized Innovations
Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using GARCH OxMetrics – An Empirical Example
Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using EViews – An Empirical Example
Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using EViews – The LogL Object
Volatility Forecasting: An Empirical Example Using GARCH Ox
Intra-Day Realized Volatility Models
Realized Volatility
Intra-Day Volatility Models
Intra-Day Realized Volatility & ARFIMAX Models in GARCH OxMetrics – An Empirical example
Applications in Value-at-Risk, Expected Shortfalls, Options Pricing
One-day-ahead Value-at-Risk Forecasting
One-day-ahead Expected Shortfalls Forecasting
FTSE Index: One-step-ahead Value-at-Risk and Expected Shortfall Forecasting
Multi-period Value-at-Risk and Expected Shortfalls Forecasting
ARCH Volatility Forecasts in Black and Scholes Option Pricing
ARCH Option Pricing Formulas
Implied Volatility Indices and ARCH Models
Implied Volatility
The VIX Index
The Implied Volatility Index as an Explanatory Variable
ARFIMAX Modeling for Implied Volatility Index
ARCH Model Evaluation and Selection
Evaluation of ARCH Models
Selection of ARCH Models
Application of Loss Functions as Methods of Model Selection.
The SPA Test for VaR and Expected Shortfalls
Multivariate ARCH Models
Model Specifications
Maximum Likelihood Estimation
Estimating Multivariate ARCH Models Using EViews
Estimating Multivariate ARCH Models Using GARCH .
Evaluation of Multivariate ARCH Models
References
Author Index
Subject Index.