Build ARCH and GARCH Models in Time Series using Python | Machine Learning Full Project

In this video, we build ARCH and GARCH models in Python to forecast stock market volatility using real financial time series data.

This tutorial is designed for absolute beginners, so every concept is explained clearly — from theory to practical coding.

We start by understanding what volatility means in financial markets and why it is important for risk management and trading decisions. Then we move step by step through data preprocessing, exploratory data analysis, stationarity testing, and volatility modeling using ARCH and GARCH.

By the end of this video, you will clearly understand how volatility behaves in financial time series and how ARCH and GARCH models help in forecasting future market fluctuations.

📌 What you will learn in this project
What volatility means in stock markets
Difference between ARCH and GARCH models
How to preprocess stock market data in Python
How to calculate daily returns and rolling volatility
How to detect and handle outliers
How to test stationarity using the ADF test
How to build ARCH and GARCH models
How to forecast volatility
How to compare model performance using MSE and MAE

🧰 Tools & Libraries Used
Python
Pandas & NumPy
Matplotlib & Seaborn
Statsmodels
ARCH library
Scikit-learn

🎓 Who should watch this video
Beginners in Time Series Analysis
Students learning Financial Data Science
Anyone interested in stock market volatility
Python learners working on real-world projects

Why this project matters
Volatility forecasting is widely used in trading, portfolio management, and risk analysis. Understanding ARCH and GARCH models is a key skill for anyone working with financial time series data.

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Full Project and Dataset -
https://www.aionlinecourse.com/ai-pro...


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