Equity Option Implied Volatility Analytics with Python - PyData Singapore

[@thetagang6854]

This is absolute gold. Thank you

[@Surfturf88]

Thank you so much for this video Jason. I have recently started using python and this is going to help a lot.

[@fabioladjouidje4505]

Please Sir, can you load a video where you calibrate hull white model using swaptions?

[@ballievlogs5515]

40:33 Makes sense but classic books teach you the smile is centered around the strike.Based on the data of IBM, you are correct..

[@saidn.4149]

The smile is the implied volatility as a function of the strike. Therefore for each strike, you have an implied volatility.

[@ballievlogs5515]

@@saidn.4149 Yes, I was a bit confused as the implied vol curve skew for a currency pair typically shows a true symmetrical 'smile'. Whereas for equity or commodities it can be skewed to the right or left ( a 'smirk' rather than a 'smile' ). But I wrongly generalized the symmetrical skew to be true for all underlyings, and afterall its a simplification within theory books. IMHO In practice you'd want to plot it in Python to see the true shape ;).

[@Med.El-amine]

Thank u sir ..can you give me Python code for calculate options pricing using Heston model with MLE and Monte Carlo method estimating parameters.. thanks my professor ❤

[@christianc8265]

does anyone know where we can find the notebook of this presentation?

[@katherinedeng2174]

This video is awesome !!

[@ayindesunday336]

This is really helpful.

[@Dinesh-dp4ov]

CAN YOU LOAD VIDEO FOR CALCULATING VOLITILITY USING VOLLIB LIBRARY IN PYTHON

[@comp5434]

from py_vollib.black_scholes.implied_volatility import * ivcall = implied_volatility(option price, underlying ltp, strike, days to expiry in years, interest rate,'c') ivput = implied_volatility(option price, underlying ltp, strike, days to expiry in years, interest rate,'p')

[@2en1th]

Has anyone ever saved a copy of his github repo and would you please share it with me? thank you!

[@aaronkoubi1843]

any luck finding it?