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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
The stationary solutions
Properties under the log-normal assumption
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
Effect of MS noise on realized volatility
Realized kernels
Bandwidth selection for realized kernels
Application
Summary of the general findings
Detailed analysis of two challenging cases
Further analysis using the Semi-FI-Log-ACD
Final remarks
A comparison study of realized kernels using different sampling frequencies
Introduction
Realized measures
Different sampling schemes
The effect of microstructure noise
Bandwidth selection for realized kernels
Realized kernels
Bandwidth selection under i.i.d. noise
Bandwidth selection under dependent noise
Application
Implementation of algorithms
Comparison of realized estimators based on Value-at-Risk
Final remarks
Appendix to Chapter 4
Conclusion
Bibliography
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