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Forecasting volatility in the financial markets [[electronic resource] /] / edited by John Knight, Stephen Satchell
| Forecasting volatility in the financial markets [[electronic resource] /] / edited by John Knight, Stephen Satchell |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 |
| Descrizione fisica | 1 online resource (428 p.) |
| Disciplina | 332.66/2042 |
| Altri autori (Persone) |
KnightJohn L
SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico |
Options (Finance) - Mathematical models
Securities - Prices - Mathematical models Stock price forecasting - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-96289-5
9786610962891 0-08-047142-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910457681603321 |
| Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting volatility in the financial markets [[electronic resource] /] / edited by John Knight, Stephen Satchell
| Forecasting volatility in the financial markets [[electronic resource] /] / edited by John Knight, Stephen Satchell |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 |
| Descrizione fisica | 1 online resource (428 p.) |
| Disciplina | 332.66/2042 |
| Altri autori (Persone) |
KnightJohn L
SatchellStephen <1949-> |
| Collana | Quantitative finance series |
| Soggetto topico |
Options (Finance) - Mathematical models
Securities - Prices - Mathematical models Stock price forecasting - Mathematical models |
| ISBN |
1-280-96289-5
9786610962891 0-08-047142-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910784357303321 |
| Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting volatility in the financial markets / / edited by John Knight, Stephen Satchell
| Forecasting volatility in the financial markets / / edited by John Knight, Stephen Satchell |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 |
| Descrizione fisica | 1 online resource (428 p.) |
| Disciplina | 332.66/2042 |
| Altri autori (Persone) |
KnightJohn L
SatchellS (Stephen) |
| Collana | Quantitative finance series |
| Soggetto topico |
Options (Finance) - Mathematical models
Securities - Prices - Mathematical models Stock price forecasting - Mathematical models |
| ISBN |
1-280-96289-5
9786610962891 0-08-047142-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910824456303321 |
| Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||