/MQP2019

pricing engines

Primary LanguageJupyter NotebookMIT LicenseMIT

MQP2019

Python basics on Jupyter notebook with Github and colab

In this section, we will get familiar with python language with Jupyter notebook through some financial applications.

Machine learning

  • linear algebra with tensorflow package - ipynb
  • linear regression with pytorch - ipynb
  • experiments to some polynomial functions - ipynb
  • learning linear function - ipynb
  • learning quadratic function - doc
  • learning second order ODE - ipynb

Financial pricing engines

  • BSM pricing engine -ipynb
  • Greeks on BSM pricing - ipynb
  • A short descriptions and todo list on CRR model - pdf
  • Arbitrage theory on discrete model - ipynb
  • CRR European/American Call/Put price - ipynb
  • Wrap up - pdf
  • Implied volatility - ipynb

References: