Course
GARCH Models in Python
IntermediateSkill Level
Updated 06/2022PythonApplied Finance4 hr15 videos54 Exercises3,950 XP10,454Statement of Accomplishment
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Prerequisites
Time Series Analysis in Python1
GARCH Model Fundamentals
What are GARCH models, what are they used for, and how can you implement them in Python? After completing this first chapter you’ll be able to confidently answer all these questions.
2
GARCH Model Configuration
A normal GARCH model is not representative of the real financial data, whose distributions frequently exhibit fat tails, skewness, and asymmetric shocks. In this chapter, you’ll learn how to define better GARCH models with more realistic assumptions. You’ll also learn how to make more sophisticated volatility forecasts with rolling window approaches.
3
Model Performance Evaluation
This chapter introduces you to the KISS principle of data science modeling. You’ll learn how to use p-values and t-statistics to simplify model configuration, use ACF plot, Ljung-Box test to verify model assumptions and use likelihood and information criteria for model selection.
4
GARCH in Action
In this final chapter, you’ll learn how to apply the GARCH models you’ve previously learned to practical financial world scenarios. You’ll develop your skills as you become more familiar with VaR in risk management, dynamic covariance in asset allocation, and dynamic Beta in portfolio management.
GARCH Models in Python
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FAQs
What financial datasets are used in this course?
You work with real-world data including historical Tesla stock prices, stock indices, cryptocurrencies, and foreign exchange rates to practice building and evaluating GARCH models.
What can I do with GARCH models after completing this course?
You will be able to forecast volatility, calculate Value-at-Risk for risk management, estimate dynamic covariance for asset allocation, and compute dynamic Beta for portfolio management.
Do I need time series analysis experience before enrolling?
Yes. The prerequisites include Time Series Analysis in Python and Manipulating Time Series Data in Python, along with pandas and Intermediate Python.
Does the course cover model selection and evaluation?
Yes. Chapter 3 teaches you to use p-values, t-statistics, ACF plots, the Ljung-Box test, and information criteria like AIC and BIC to select and validate your models.
How does this course handle real-world features like fat tails and asymmetric shocks?
Chapter 2 covers configuring GARCH models with more realistic distribution assumptions, including fat-tailed and skewed distributions, plus rolling window forecasting approaches.
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