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 • financial volatility modeling using parametric models
skills learned
volatility modeling usingvolatility modeling using using support vector machines with different kernels, neural networks, and deep learning models using support vector machines with different kernels • neural networks • deep learning models
Abdullah Karasan
1 week · 8-10 hours per week · BEGINNER

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Now that you’ve tackled volatility modeling in a traditional way, in this liveProject, your employer has challenged you to uplevel your volatility modeling by taking a more dynamic, data-dependent approach. By the end of this project, you’ll have firsthand experience modeling volatility using support vector machines with different kernels, neural networks, and deep learning models. What’s more, you’ll have the skills to determine if the machine learning-based models outperform the traditional parametric models.

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

book and video resources

When you start your liveProject, you get full access to the following books and videos 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 with an intermediate knowledge of data science. To begin this liveProject, you will need to be familiar with the following:

  • Intermediate Python knowledge and skills
  • Intermediate data science knowledge and skills
  • Jupyter Notebook
  • sklearn for running classical time series analysis and stationary analysis
  • Matplotlib for visualization
  • TensorFlow for deep learning

you will learn

  • Using support vector machines, neural networks, and deep learning to model financial volatility
  • Hyperparameter tuning
  • Using machine and deep learning libraries TensorFlow and sklearn
  • Comparing performance of traditional models to machine learning models for modeling financial volatility


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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