Forecasting Volatility in the Financial Markets
| Year of publication: |
2011 ; 3rd ed
|
|---|---|
| Authors: | Satchell, Stephen |
| Other Persons: | Knight, John (contributor) |
| Publisher: |
Burlington : Elsevier Science |
| Subject: | Volatilität | Volatility | Prognoseverfahren | Forecasting model | Finanzmarkt | Financial market | ARCH-Modell | ARCH model | Kapitalmarkttheorie | Financial economics | Modellierung | Scientific modelling |
| Description of contents: |
4.4 Initial data analysis
|
| Extent: | 428 p. |
|---|---|
| Series: | |
| Type of publication: | Book / Working Paper |
| Language: | English |
| Notes: | Description based upon print version of record Front Cover; Forecasting Volatility in the Financial Markets; Copyright Page; Table of Contents; List of contributors; Preface to Third Edition; Introduction; Chapter 1 Volatility modelling and forecasting in finance; 1.1 Introduction; 1.2 Autoregressive moving average models; 1.3 Changes in volatility; 1.3.1 Volatility in financial time series: stylized facts; 1.3.2 The basic set-up; 1.4 ARCH models; 1.4.1 Generalized ARCH; 1.4.2 Integrated ARCH; 1.4.3 Exponential ARCH; 1.4.4 ARCH-M model; 1.4.5 Fractionally integrated ARCH; 1.4.6 Other univariate ARCH formulations 1.4.7 Multivariate ARCH models1.5 Stochastic variance models; 1.5.1 From continuous time financial models to discrete time SV models; 1.5.2 Persistence and the SV model; 1.5.3 Long memory SV models; 1.5.4 Risk-return trade-off in SV models; 1.5.5 Multivariate SV models; 1.6 Structural changes in the underlying process; 1.6.1 Regime switching models; 1.6.2 Extensions of the regime switching models; 1.7 Threshold models; 1.7.1 Self-exciting threshold models; 1.7.2 Open loop threshold models; 1.7.3 Closed loop threshold models; 1.7.4 Smooth threshold autoregressive models 1.7.5 Identification in SETAR models1.7.6 A threshold AR(1) model; 1.7.7 A threshold MA model; 1.7.8 Threshold models and asymmetries in volatility; 1.7.9 Testing for non-linearity; 1.7.10 Threshold estimation and prediction of TAR models; 1.8 Volatility forecasting; 1.8.1 Volatility forecasting based on time-series models; 1.8.2 Volatility forecasting based on option ISD (Implied Standard Deviation); 1.9 Conclusion; References and further reading; Notes; Chapter 2 What good is a volatility model?; Abstract; 2.1 Introduction; 2.1.1 Notation; 2.1.2 Types of volatility models 2.2 Stylized facts about asset price volatility2.2.1 Volatility exhibits persistence; 2.2.2 Volatility is mean reverting; 2.2.3 Innovations may have an asymmetric impact on volatility; 2.2.4 Exogenous variables may influence volatility; 2.2.5 Tail probabilities; 2.2.6 Forecast evaluation; 2.3 An empirical example; 2.3.1 Summary of the data; 2.3.2 A volatility model; 2.3.3 Mean reversion and persistence in volatility; 2.3.4 An asymmetric volatility model; 2.3.5 A model with exogenous volatility regressors; 2.3.6 Aggregation of volatility models 2.4 Conclusions and challenges for future researchReferences; Notes; Chapter 3 Applications of portfolio variety; Abstract; 3.1 Introduction; 3.2 Some applications of variety; 3.3 Empirical research on variety; 3.4 Variety and risk estimation; 3.5 Variety as an explanation of active management styles; 3.6 Summary; References; Chapter 4 A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices; 4.1 Introduction; 4.2 Data; 4.3 Theory and empirical methodology; 4.3.1 Realized variance; 4.3.2 Optimal sampling frequency; 4.3.3 Estimation; 4.3.4 Forecasting 4.4 Initial data analysis |
| ISBN: | 978-0-08-047142-6 ; 978-0-7506-6942-9 |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012677066