Heston and Nandi Garch Model implementation in Python -- 2
Budget: €36 – €0 EUR
I am implementing on Python the GARCH model by Heston and Nandi (2000).
The objective is running a returns-based estimation of the model and an options-based estimation of the model.
Then, test its option pricing formula over options market data from the S&P 500.
I have already implemented successfully the returns-based estimation. Now, I have to build an algorithm to minimise the MSE between the option prices estimated by the model and those observed in the market.
I would need help from someone with a good knowledge of optimization techniques on Python.
HNGarch. py: routine to estimate the model based on returns of the SPX. The main function I use is GARCH_fit
hngoption.py: the formula HNC is the pricing formula is use for options. The inputs are the parameters estimated at the previous step.
thesis.py: the script I am using to conduct the emprical analysis.
Feel free to contact me for further information
The objective is running a returns-based estimation of the model and an options-based estimation of the model.
Then, test its option pricing formula over options market data from the S&P 500.
I have already implemented successfully the returns-based estimation. Now, I have to build an algorithm to minimise the MSE between the option prices estimated by the model and those observed in the market.
I would need help from someone with a good knowledge of optimization techniques on Python.
HNGarch. py: routine to estimate the model based on returns of the SPX. The main function I use is GARCH_fit
hngoption.py: the formula HNC is the pricing formula is use for options. The inputs are the parameters estimated at the previous step.
thesis.py: the script I am using to conduct the emprical analysis.
Feel free to contact me for further information