協整理論與波動模型-金融時間序列分析及應用(第二版)（簡體書）

本書論述了時間序列的協整理論和金融時間序列波動性模型的原理、方法和實際應用。在時間序列的協整理論方面，包括單位根過程的極限分布和檢驗，單方程和系統方程協整關係的估計和檢驗，非線性、長記憶協整關係的建模和檢驗問題，協整系統的貝葉斯分析及變結構協整的理論、方法等。在金融時間序列波動模型方面，包括自回歸條件異方差（ARCH）模型的各類一維和多維模型體系及各類隨機波動（SV）模型的性質、模型參數估計和檢驗問題，討論了變結構波動模型的建模及其應用等。金融波動性問題是當今金融分析中的重要課題，本書探討了金融波動及其持續性的市場機制，建立了在金融波動持續性基礎上的資本資產定價模型和金融風險規避策略等。書中詳細討論了高頻金融時間序列分析與建模問題，研究了各類高頻時間序列已實現波動率的計算方法和統計性質，討論了超高頻數據持續期的ACD類和SCD類兩類模型。書中還討論了小波方法在金融時間序列波動分析和建模方面的應用;討論了各類連續時間資產收益模型及參數估計的MCMC方法。
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。

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
　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 of fractionally integrated 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 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
　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
　7.4 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
　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