After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Volatility is an essential concept in finance, which is why GARCH models in Python are a popular choice for forecasting changes in variance, specifically when working with time-series data that are time-dependant. This course will show you how and when to implement GARCH models, how to specify model assumptions, and how to make volatility forecasts and evaluate model performance. Using real-world data, including historical Tesla stock prices, you’ll gain hands-on experience of how to better quantify portfolio risks, through calculations of Value-at-Risk, covariance, and stock Beta. You’ll also apply what you’ve learned to a wide range of assets, including stocks, indices, cryptocurrencies, and foreign exchange, preparing you to go forth and use GARCH models.
Prerequisites
Curriculum
Course outline
1
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.
- Why do we need GARCH models50 XP
- Understand volatility50 XP
- Observe volatility clustering100 XP
- Calculate volatility100 XP
- What are ARCH and GARCH50 XP
- Review GARCH model basics50 XP
- Simulate ARCH and GARCH series100 XP
- Observe the impact of model parameters100 XP
- How to implement GARCH models in Python50 XP
- Review "arch" documentation50 XP
- Implement a basic GARCH model100 XP
- Make forecast with GARCH models100 XP
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
Course
Complete
