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# GARCH Models in Python

Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

4 Hours15 Videos54 Exercises

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## 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.

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1. 1

### GARCH Model Fundamentals

Free

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.

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Why do we need GARCH models
50 xp
Understand volatility
50 xp
Observe volatility clustering
100 xp
Calculate volatility
100 xp
What are ARCH and GARCH
50 xp
Review GARCH model basics
50 xp
Simulate ARCH and GARCH series
100 xp
Observe the impact of model parameters
100 xp
How to implement GARCH models in Python
50 xp
Review "arch" documentation
50 xp
Implement a basic GARCH model
100 xp
Make forecast with GARCH models
100 xp
2. 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. 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. 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.

### In the following Tracks

#### Applied Finance in Python

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Datasets

SP500Tesla stock priceBitcoin priceForeign exchange data

Collaborators

Chelsea Yang

Data Science Instructor

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