Further development of semiparametric volatility models and their applications to value at risk and expected shortfall / von Xuehai Zhang, M.A. Paderborn, 2019
Inhalt
- List of Tables
- List of Figures
- List of Abbreviations
- Introduction
- Parametric and Semiparametric Models
- Introduction
- Overview of the volatility models
- The ACD model
- The semiparametric GARCH model
- The semiparametric ACD model
- Final remarks
- SemiGARCH models based on Box-Cox transformation
- Introduction
- The SemiGARCH model with Box-Cox transformation
- The semiparametric estimation procedure
- Estimation of s(t)
- Semiparametric estimation of a given model
- The bandwidth estimation algorithm
- The power transformation parameter estimation algorithm
- A simple stationary test
- Applications
- Final remarks
- VaR and ES under general Semiparametric GARCH models
- Introduction
- VaR and ES with semiparametric processes
- The Backtesting of VaR and ES
- The loss function
- The empirical study
- Final remarks
- Modeling high-frequency returns using general SemiGARCH models
- Semiparametric MEM with Power Transformation
- Introduction
- The model
- The model estimation and properties
- The data-driven algorithms
- The empirical examples
- Final remarks
- Further topics
- Introduction
- The sampling schemes
- The SemiMEM models to the high-frequency data
- The empirical analysis
- The data
- The analysis of the intraday trading volume
- The analysis of the intraday trading duration
- The analysis of the intraday realized volatility
- Final remarks
- Concluding remarks
- References
- Appendices