Modelling and forecasting financial and economic time series using different semiparametric ACD models / von M.A. Sarah Forstinger. Paderborn, 2018
Inhalt
- List of figures
- List of tables
- Introduction
- On the iterative plug-in algorithm
- Introduction
- The Semi-ACD model for diurnal durations
- The bandwidth selection procedure
- The simulation study
- Description of the simulation study
- Performance of the selected bandwidth
- Goodness of fit of (t)
- Performance of the ACD parameter estimation
- Application to simulated and real data examples
- Conclusion
- Appendix of Chapter 2
- A semiparametric multiplicative error model
- Introduction
- The proposed models
- Correlation structure under log-normal assumption
- The two-stage estimation procedure
- Practical Implementation
- Application to real financial data
- Conclusion
- Appendix of Chapter 3
- Forecasting non-negative financial processes
- Introduction
- The Semi-ACD and Semi-Log-ACD model
- Forecasting methods
- Application to real financial data
- Conclusion
- Appendix of Chapter 4
- Forecasting Economic Growth Processes
- Introduction
- Data and semiparametric model
- Proposed forecasting approaches
- Point prediction based on the semiparametric model
- Some random walk models
- Individual forecasts, prediction intervals and densities
- Application to the selected examples
- Conclusion
- Appendix of Chapter 5
- Future Research Questions
- A Semi-Log-GARCH model extension
- The Log-sinh-arcsinh-transformation
- Neural network GDP forecasts
- Miscellaneous research topics
- Local bandwidth factor for IPI improvement
- Model parameter estimation
- Block-bootstrap for forecasting
- Appendix of Chapter 6
- Summary of chapters and conclusion
- Bibliography
- Supplementary Appendix
- Ehrenwörtliche Erklärung