Eikon Data API - Python Quants Tutorial 12 - Derivative Analytics II : Calibrating an Options Pricing Model

Presenter: Dr. Yves Hilpisch of The Python Quants           

Length: 19 mins
In this second part of the Derivative Analytics tutorial we build upon the ease with which the Eikon Data API allows you to work with Options Chains by showing how to calibrate an options pricing model. We will show that this model is capable of approximating prices observed in the real world. We combine the Merton (1976) characteristic function with the Lewis (2001) integration function and finally evaluate the integral via numerical quadrature. To conclude, we compare the model-based prices to those observed in the markets and draw some conclusions.

  • Retrieving options data based on chain RICs
  • Implementing Merton (1976) Jump-Diffusion Model in Python to calculate a modelled option valuation
  • Calibration of the model, based on the Root Mean Squared Error (RMSE), analysis of the results                                                                                                                                                  Next Tutorial>>>