MathePhysics/UROP-2022
Option Pricing with Machine Learning Methods
This project offers an alternative way to calculate the fair price of financial options, including both standard (vanilla) and multi-asset (basket) options. By leveraging machine learning models, it takes historical option data and outputs more accurate and often faster price estimations compared to traditional financial models. Traders, quantitative analysts, and financial engineers who deal with options pricing would find this valuable.
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Use this if you need to price European call and put options more accurately and efficiently than with standard Black-Scholes or Heston models.
Not ideal if you require explainability or a confidence interval for every price estimate, as some models here provide only point estimates.
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Last pushed
Jun 18, 2024
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