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Statistics
Time Series Analysis
1. Introduction to Time Series
2. Foundational Concepts and Data Preparation
3. Descriptive Analysis and Decomposition
4. Stationarity and Unit Root Analysis
5. Autocorrelation and Dependence Structure
6. Classical Forecasting Methods
7. ARIMA Modeling
8. Seasonal ARIMA Models
9. Advanced Univariate Models
10. Volatility Modeling
11. Multivariate Time Series
12. State Space Models and Kalman Filtering
13. Machine Learning for Time Series
14. Forecasting Evaluation and Model Selection
15. Practical Forecasting Considerations
16. Software and Implementation
Volatility Modeling
Heteroskedasticity in Time Series
Conditional vs. Unconditional Variance
Volatility Clustering
ARCH Effects Testing
ARCH Models
Basic ARCH Structure
Parameter Estimation
Forecasting Volatility
Model Extensions
GARCH Models
GARCH(1,1) Model
Higher-Order GARCH
Parameter Constraints
Volatility Forecasting
GARCH Extensions
EGARCH Models
GJR-GARCH
TGARCH
Component GARCH
Multivariate GARCH
BEKK Models
DCC Models
Factor GARCH
Applications
Financial Risk Management
Option Pricing
Portfolio Optimization
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9. Advanced Univariate Models
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11. Multivariate Time Series