內容簡介
協整理論與波動模型本書可作為數量經濟學研究人員、有關教師、經濟和金融工作者的參考書，亦可作為相關領域研究生的教學參考書。
圖書目錄
Chapter 1Time Series Analysis
1.1 General time series models
1.2 Vector stationary time series·vectorautoregressive model
1.3 Non?stationary stochastic processes and integrated time series
1.4 Long memory time series
References
Chapter 2Tests of Unit Root Processes
2.1 Unit root processes
2.2 Limiting distribution of integrated processes
2.3 Tests of unit root processes
2.4 Vector autoregressive processes with unit root
References
Chapter 3Cointegration Theory and Methodology
3.1 Cointegration and error correction model
3.2 Estimation and tests of cointegration relationship in single equation
3.3 Estimation and tests of cointegration relationship in simultaneous equation system
3.4 Bayesian analysis of cointegrated system
3.5 Linear conintegration analysis offractionallyintegrated vector time series
3.6 Forecasting of cointegrated system
3.7 Nonlinear transformation of integrated time series1
References
Chapter 4Seasonal Integration and Cointegration
4.1 Seasonal integration, cointegration and tests
4.2 Bayesian tests of seasonal cointegration
Appendix: Lagrange polynomial approximation theorem
References
Chapter 5Nonlinear Cointegration Theory
5.1 Definition of nonlinear cointegration
5.2 Estimation and tests of nonlinear cointegration relationship
5.3 Existence of nonlinear cointegration relationship
5.4 Nonlinear cointegration modeling based on wavelet neural network
5.5 Nonlinear error correction models of linearly cointegrated system
5.6 Nonlinear cointegration relationship in long memory vector time series
5.7 Cointegration with structure changes and modeling
References
Chapter 6ARCH Class Models
6.1 Short memory ARCH class models
6.2 Long memory ARCH class models
6.3 Fractionally integratedaugmentGARCH?M model
6.4 GARCH class models for panel data
6.5 Statistical properties of GARCH model
6.6 Modeling of ARCH class models
6.7 Diagnostic analysis and structure change modeling of ARCH class models
6.8 Stochastic differential equation of GARCH process
6.9 Unit root tests with conditional heteroskedasticity
6.10 Vector GARCH models and modeling
6.11 Persistence and co?persistence in vector GARCH process
6.12 Persistence and co?persistence in conditional moments of time series
References
Chapter 7Stochastic Volatility Models
7.1 Basic SV models and statistical properties
7.2 Extended SV models
7.3 Parameters estimation of SV models
7.4qmlestimation based on THGA and Monte Carlo
7.5 Estimation of long memory SV models and applications
7.6 SV models with structure changes
7.7 Aggregation and marginalization of SV models
7.8 Persistence and co?persistence in SV models
7.9 Comparison of SV and GARCH models
7.10 Square?root stochastic autoregressive volatility model
References
Chapter 8 Analysis of Financial Volatility
Chapter 9 Analysis and Modeling for High?Frequency Financial Time Series
Chapter 10 Wavelet Methods for Financial Time Series Analysis
Chapter 11 Continuous Time Models and its Applications
