协整理论与波动模型

Chapter 1Time Series Analysis　1.1 General time series models　1.2 Vector stationary time series·vector autoregressive model　1.3 Nonstationary stochastic processes and integrated time series　1.4 Long memory time series　ReferencesChapter 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　ReferencesChapter 3Cointegration Theory and Methodology　3.1 Cointegration and error correction model　3.2 Estimation and tests of cointegration relationship in single equation

Chapter 1Time Series Analysis　1.1 General time series models　1.2 Vector stationary time series·vector autoregressive model　1.3 Nonstationary stochastic processes and integrated time series　1.4 Long memory time series　ReferencesChapter 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　ReferencesChapter 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 of fractionally integrated vector time series　3.6 Forecasting of cointegrated system　3.7 Nonlinear transformation of integrated time series1　ReferencesChapter 4Seasonal Integration and Cointegration　4.1 Seasonal integration, cointegration and tests　4.2 Bayesian tests of seasonal cointegration　Appendix: Lagrange polynomial approximation theorem　ReferencesChapter 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　ReferencesChapter 6ARCH Class Models　6.1 Short memory ARCH class models　6.2 Long memory ARCH class models　6.3 Fractionally integrated augment GARCH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　ReferencesChapter 7Stochastic Volatility Models　7.1 Basic SV models and statistical properties　7.2 Extended SV models　7.3 Parameters estimation of SV models　7.4 QML estimation 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　ReferencesChapter 8 Analysis of Financial VolatilityChapter 9 Analysis and Modeling for HighFrequency Financial Time SeriesChapter 10 Wavelet Methods for Financial Time Series AnalysisChapter 11 Continuous Time Models and its Applications

本书论述了时间序列的协整理论和金融时间序列波动性模型的原理、方法和实际应用。在时间序列的协整理论方面，包括单位根过程的极限分布和检验，单方程和系统方程协整关系的估计和检验，非线性、长记忆协整关系的建模和检验问题，协整系统的贝叶斯分析及变结构协整的理论、方法等。在金融时间序列波动模型方面，包括自回归条件异方差（ARCH）模型的各类一维和多维模型体系及各类随机波动（SV）模型的性质、模型参数估计和检验问题，讨论了变结构波动模型的建模及其应用等。金融波动性问题是当今金融分析中的重要课题，本书探讨了金融波动及其持续性的市场机制，建立了在金融波动持续性基础上的资本资产定价模型和金融风险规避策略等。书中详细讨论了高频金融时间序列分析与建模问题，研究了各类高频时间序列已实现波动率的计算方法和统计性质，讨论了超高频数据持续期的ACD类和SCD类两类模型。书中还讨论了小波方法在金融时间序列波动分析和建模方面的应用;讨论了各类连续时间资产收益模型及参数估计的MCMC方法。　　本书可作为数量经济学研究人员、有关教师、经济和金融工作者的参考书，亦可作为相关领域研究生的教学参考书。