UsefulLinks
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
10.
Volatility Modeling
10.1.
Heteroskedasticity in Time Series
10.1.1.
Conditional vs. Unconditional Variance
10.1.2.
Volatility Clustering
10.1.3.
ARCH Effects Testing
10.2.
ARCH Models
10.2.1.
Basic ARCH Structure
10.2.2.
Parameter Estimation
10.2.3.
Forecasting Volatility
10.2.4.
Model Extensions
10.3.
GARCH Models
10.3.1.
GARCH(1,1) Model
10.3.2.
Higher-Order GARCH
10.3.3.
Parameter Constraints
10.3.4.
Volatility Forecasting
10.4.
GARCH Extensions
10.4.1.
EGARCH Models
10.4.2.
GJR-GARCH
10.4.3.
TGARCH
10.4.4.
Component GARCH
10.5.
Multivariate GARCH
10.5.1.
BEKK Models
10.5.2.
DCC Models
10.5.3.
Factor GARCH
10.6.
Applications
10.6.1.
Financial Risk Management
10.6.2.
Option Pricing
10.6.3.
Portfolio Optimization
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9. Advanced Univariate Models
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11. Multivariate Time Series