Evdokia Xekalaki, Stavros Degiannakis

ARCH Models for Financial Applications (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 558
• Erscheinungstermin: 3. März 2010
• Englisch
• ISBN-13: 9780470688021
• Artikelnr.: 37298667

Autorenporträt

Evdokia Xekalaki, Department of Statistics, Athens University of Economics and Business Professor Xekalaki has been teaching for nearly 30 years, and in that time has held such positions as Director of the graduate program, consultant to EUROSTAT and twice Chair of the Dept of Statistics at AUEB. She has published more than 50 papers in numerous international journals and has presented papers at many international conferences. She is also the Chief Editor of the journal Quality Technology and Quantitative Management and on the Editorial Board for the Journal of Applied Stochastic Models in Business and Industry. Stavros Degiannakis, Department of Statistics, Athens University of Economics and Business Adjunct lecturer in applied econometrics, Dr Degiannakis acquired his PhD last year, and has already had eight articles published in seven journals, and eight other papers presented at a variety of international conferences.

Inhaltsangabe

Prologue. Notation. 1 What is an ARCH process? 1.1 Introduction. 1.2 The
Autoregressive Conditionally Heteroskedastic 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 and Conditional
Mean. 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. 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. 4 Volatility Forecasting: An Empirical Example Using
EViews 6. 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 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. 6 Volatility
Forecasting: An Empirical Example Using G@RCH Ox. 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. 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. 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
Model Selection.. 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.