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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 Exercises5,252 Learners3950 XPApplied Finance Track

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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


    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.

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  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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  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.

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In the following tracks

Applied Finance


AAN94Adel Nehmeamy-4121b590-cc52-442a-9779-03eb58089e08Amy Peterson
Chelsea Yang Headshot

Chelsea 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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