Section 1: Financial Time Series Analysis

Overview

Section 1 covers the fundamentals to advanced models in financial time series data analysis. From univariate time series analysis (ARIMA, GARCH) to multivariate time series analysis (VAR, VECM), we implement models applicable to real financial data using Python.

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Main Chapters

Chapter 1: Fundamentals of Time Series Data Analysis

• Time series decomposition (Trend, Seasonality, Residual)
• Stationary vs non-stationary data
• Basic visualization techniques

Chapter 2: Advanced Time Series Analysis

• ADF test (Augmented Dickey-Fuller Test) - Stationarity testing
• AR, MA models
• ACF/PACF plot interpretation

Chapter 3: Univariate Time Series Analysis

• ARIMA models
• Auto-ARIMA
• Model selection (AIC vs BIC)

Chapter 4: Advanced Volatility Modeling and Forecasting

• ARCH models
• GARCH models
• ARIMA-GARCH hybrid models

Chapter 5: Multivariate Time Series Analysis and Advanced Models

• VAR models (Vector Autoregression)
• Granger Causality
• VARMA models

Chapter 6: Advanced Multivariate Time Series Analysis

• VECM models (Vector Error Correction Model)
• Johansen cointegration test
• VAR IRF (Impulse Response Function) and FEVD (Forecast Error Variance Decomposition)

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Key Concepts

Stationarity

A fundamental assumption in time series analysis. A time series where the mean and variance do not change over time is called a stationary time series. Most time series models assume stationarity.

Cointegration

When a linear combination of multiple non-stationary time series becomes a stationary time series, these time series are said to be in a cointegration relationship. This is a core concept of the VECM model.

Volatility Clustering

A phenomenon observed in financial time series where high volatility tends to follow high volatility, and low volatility tends to follow low volatility. This is modeled using GARCH models.

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Learning Resources