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eISBN : 978-1118271995

Sommaire : 1. Volatility Models
1.1 Introduction
1.2 GARCH 1
1.3 Stochastic Volatility
1.4 Realized Volatility

Part I. ARCH and SV

2. Nonlinear ARCH Models
2.1 Introduction
2.2 Standard GARCH model
2.3 Predecessors to Nonlinear GARCH
2.4 Nonlinear ARCH and GARCH
2.5 Testing
2.6 Estimation
2.7 Forecasting
2.8 Multiplicative Decomposition
2.9 Conclusion

3. Mixture and Regime-switching GARCH Models
3.1 Introduction
3.2 Regime-switching GARCH models
3.3 Stationarity and Moment Structure
3.4 Regime Inference, Likelihood Functions, and Volatility Forecasting
3.5 Application of Mixture GARCH Models
3.6 Conclusion

4. Forecasting High Dimensional Covariance Matrices
4.1 Introduction
4.2 Notation
4.3 Rolling-Window Forecasts
4.4 Dynamic Models
4.5 High-Frequency Based Forecasts
4.6 Forecast Evaluation
4.7 Conclusion

5. Mean, Volatility and Skewness Spillovers in Equity Markets
5.1 Introduction
5.2 Data and Summary Statistics
5.3 Empirical Results
5.4 Conclusion

6. Relating Stochastic Volatility Estimation Methods
6.1 Introduction
6.2 Theory and Methodology
6.3 Comparison of Methods
6.4 Estimating Volatility Models in Practice
6.5 Conclusion

7. Multivariate Stochastic Volatility Models
7.1 Introduction
7.2 MSV model
7.3 Factor MSV model
7.4 Applications to Stock Indices Returns
7.5 Conclusion

8. Model Selection and Testing of Volatility Models
8.1 Introduction
8.2 Model Selection and Testing
8.3 Empirical Example
8.4 Conclusion

Part II. Other models and methods

9. Multiplicative Error Models
9.1 Introduction
9.2 Theory and Methodology
9.3 MEM Application
9.4 MEM Extensions
9.5 Conclusion

10. Locally Stationary Volatility Modeling
10.1 Introduction
10.2 Empirical evidences
10.3 Locally Stationary Processes
10.4 Locally Stationary Volatility Models
10.5 Multivariate Models for Locally Stationary Volatility
10.6 Conclusion

11. Nonparametric and Semiparametric Volatility Models
11.1 Introduction
11.2 Nonparametric and Semiparametric Univariate Models
11.3 Nonparametric and Semiparametric Multivariate Volatility Models
11.4 Empirical Analysis
11.5 Conclusion

12. Copula-based Volatility Models
12.1 Introduction
12.2 Definition and Properties of Copulas
12.3 Estimation
12.4 Dynamic Copulas
12.5 Value-at-Risk
12.6 Multivariate Static copulas
12.7 Conclusion

Part III. Realized Volatility
13. Realized Volatility: Theory and Applications

13.1 Introduction
13.2 Modelling Framework
13.3 Issues in Handling Intra-day Transaction Databases
13.4 Realized Variance and Covariance
14.5 Modelling and Forecasting
13.6 Asset Pricing
13.7 Estimating Continuous Time Models

14. Likelihood-Based Volatility Estimators
14.1 Introduction
14.2 Volatility Estimation
14.3 Covariance Estimation
14.4 Empirical Application
14.5 Conclusion

15. HAR Modeling for Realized Volatility Forecasting
15.1 Introduction
15.2 Stylized Facts
15.3 Heterogeneity and Volatility Persistence
15.4 HAR Extensions
15.5 Multivariate Models
15.6 Applications
15.7 Conclusion

16. Forecasting volatility with MIDAS
16.1 Introduction
16.2 MIDAS Regression Models and Volatility Forecasting
16.3 Likelihood-based Methods
16.4 Multivariate Models
16.5 Conclusion

17. Jumps
17.1 Introduction
17.2 Estimators of Integrated Variance and Integrated Covariance
17.3 Testing for the Presence of Jumps
17.4 Conclusion

18. Jumps, Periodicity and Microstructure Noise
18.1 Introduction
18.2 Model
18.3 Price Jump Detection Method
18.4 Simulation Study
18.5 Comparison on NYSE-Stock Prices
18.6 Conclusion

19. Volatility Forecasts Evaluation and Comparison
19.1 Introduction
19.2 Notation
19.3 Single Forecast Evaluation
19.4 Loss Functions and the Latent Variable Problem
19.5 Pairwise Comparison
19.6 Multiple Comparison
19.7 Consistency of the Ordering and Inference on Forecast Performances
19.8 Conclusion

Index
Bibliography

Langue : Anglais

Lieu d'édition : TORONTO

Localisation : Bibliothèque Campus de Nice

Support : Numérique

Etat : Présent

Propriétaire : Bibliothèque