Data-driven realized kernels and further analysis using a semi-FI-log-ACD model / von Chen Zhou, M.Sc. Paderborn, 2018
Inhalt
- List of Figures
- List of Tables
- List of Abbreviations
- Introduction
- Volatility models
- Forecasting based on the Semi-FI-Log-ACD model
- An iterative plug-in algorithm for realized kernels
- RK under dependent noise using different sampling frequencies
- Forecasting financial market activity using a semiparametric fractionally integrated Log-ACD
- Introduction
- A semiparametric multiplicative long memory model
- Origin of long memory in aggregated financial data
- Simultaneously modeling long memory and scale change
- Properties and estimation of the models
- Forecasting based on the Semi-FI-Log-ACD
- Extrapolation of the trend function
- The best linear and approximately best linear predictors
- Approximate forecasting intervals
- Application
- Final remarks
- Appendix to Chapter 2
- An iterative plug-in algorithm for realized kernels
- Introduction
- Realized volatility and realized kernels
- Bandwidth selection for realized kernels
- Application
- Further analysis using the Semi-FI-Log-ACD
- Final remarks
- A comparison study of realized kernels using different sampling frequencies
- Introduction
- Realized measures
- Bandwidth selection for realized kernels
- Application
- Final remarks
- Appendix to Chapter 4
- Conclusion
- Bibliography