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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.

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4 Hours15 Videos54 Exercises6,671 Learners

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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.
  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.

    Play Chapter Now
    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.

    Play Chapter Now
    Distribution assumptions
    50 xp
    Fat tails and skewness
    50 xp
    Plot distribution of standardized residuals
    100 xp
    Fit a GARCH with skewed t-distribution
    100 xp
    Mean model specifications
    50 xp
    Check mean model assumptions
    50 xp
    Effect of mean model on volatility predictions
    100 xp
    Volatility models for asymmetric shocks
    50 xp
    Modeling of asymmetric responses of volatility
    50 xp
    Fit GARCH models to cryptocurrency
    100 xp
    Compare GJR-GARCH with EGARCH
    100 xp
    GARCH rolling window forecast
    50 xp
    Why use rolling window forecast
    50 xp
    Fixed rolling window forecast
    100 xp
    Compare forecast results
    100 xp
  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.

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    Significance testing of model parameters
    50 xp
    Keep it simple stupid
    50 xp
    Simplify the model with p-values
    100 xp
    Simplify the model with t-statistics
    100 xp
    Validation of GARCH model assumptions
    50 xp
    Detect autocorrelations
    50 xp
    ACF plot
    100 xp
    Ljung-Box test
    100 xp
    Goodness of fit measures
    50 xp
    Goodness of fit basics
    50 xp
    Pick a winner based on log-likelihood
    100 xp
    Pick a winner based on AIC/BIC
    100 xp
    GARCH model backtesting
    50 xp
    Backtesting basics
    50 xp
    Backtesting with MAE, MSE
    100 xp
  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.

    Play Chapter Now
    VaR in financial risk management
    50 xp
    VaR concept
    50 xp
    Compute parametric VaR
    100 xp
    Compute empirical VaR
    100 xp
    Dynamic covariance in portfolio optimization
    50 xp
    Covariance concept
    50 xp
    Compute GARCH covariance
    100 xp
    Compute dynamic portfolio variance
    100 xp
    Dynamic Beta in portfolio management
    50 xp
    Beta concept
    50 xp
    Compute dynamic stock Beta
    100 xp
    Congratulations!
    50 xp

In the following tracks

Applied Finance in Python

Datasets

SP500Tesla stock priceBitcoin priceForeign exchange data

Collaborators

Amy Peterson
Adel Nehme

Prerequisites

Time Series Analysis in Python
Chelsea Yang HeadshotChelsea Yang

Data Science Instructor

Chelsea is a senior quantitative analyst with over a decade’s experience working for top asset managers and financial institutions. She is a data science enthusiast and passionate about its application in finance. She has expertise in financial modeling, risk management, and machine learning. Chelsea holds a Master's degree in Management Information Systems from Carnegie Mellon University. In her spare time, she enjoys writing Python programs to test her trading ideas.
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