Volatility Modeling

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This project is part of the liveProject series Finance Volatility Modeling
intermediate Python • basic scikit-learn and Matplotlib • basic financial concepts • intermediate machine learning
skills learned
volatility forecasting in financial modeling using ARCH and GARCH • optimizing financial model parameters using BIC
Abdullah Karasan
1 week · 8-10 hours per week · BEGINNER

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In this liveProject, you’ll play the part of a freelance consultant who’s been hired to assess a company’s financial risk. Using the traditional volatility modeling packages ARCH and GARCH, you’ll model the volatility of S&P-500 stock prices—a good proxy for the entire financial market—and measure model performance. Then, you’ll optimize the model parameters using information criteria such as Bayesian Information Criteria (BIC). When you’ve completed the project, you’ll have a solid understanding of the logic of these traditional models and be ready to apply it to other models.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Abdullah Karasan
Abdullah Karasan was born in Berlin, Germany. After studying economics and business administration, he obtained his master's degree in applied economics from the University of Michigan, Ann Arbor, and his PhD in financial mathematics from the Middle East Technical University, Ankara. He is a former Treasury employee of Turkey and currently works as a principal data scientist at Magnimind and as a lecturer at the University of Maryland, Baltimore. He has also published several papers in the field of financial data science.


The liveProject is for intermediate Python programmers who know the basics of data science. To begin this liveProject, you will need to be familiar with the following:

  • Intermediate Python knowledge and skills
  • Jupyter Notebook
  • Basic financial concepts
  • Intermediate machine learning knowledge
  • sklearn for running classical time series analysis and stationary analysis
  • Matplotlib for visualization
  • ARCH for modeling
  • scipy.optimize.minimize for modeling volatility by hand

you will learn

  • Importing modules with Python
  • Declaring customized functions
  • Data extraction using the Yahoo Finance API
  • Predicting and modeling volatility using the parametric models ARCH and GARCH
  • Optimizing model parameters using BIC
  • Analyzing and comparing prediction results


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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