differential-machine-learning/notebooks
Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.
This project helps quantitative analysts and financial engineers apply 'differential machine learning' techniques for pricing financial derivatives and managing risk. It takes simulated financial data, often including sensitivities, and provides more accurate and faster valuations and risk factor identification than traditional methods. The user is a quantitative analyst or financial engineer working with complex financial models.
147 stars. No commits in the last 6 months.
Use this if you are a quantitative analyst seeking to improve the speed and accuracy of financial derivative pricing, risk factor analysis, and hedging strategies using advanced machine learning.
Not ideal if you are not working with financial derivatives or do not have a strong understanding of quantitative finance concepts and machine learning fundamentals.
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147
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56
Language
Jupyter Notebook
License
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Last pushed
Oct 05, 2022
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