This is a collection of Jupyter notebooks and Python files which have been used in the article:
"Signature-based models: theory and calibration"
of Christa Cuchiero, Guido Gazzani and Sara Svaluto-Ferro.
For citations:
MDPI and ACS Style
Cuchiero, C.; Gazzani, G.; Svaluto-Ferro, S. Signature-based models: theory and calibration.
@article{CGS:22,
title={{Signature-based models: theory and calibration}},
author={Cuchiero, C. and Gazzani, G. and Svaluto-Ferro S.},
journal={Preprint arXiv:2207.13136},
year={2022}}
In the present repository you will find the following material.
- Code for a Heston model, when learning the price dynamics. (Stoch_vol_regressionPrice_Heston.py)
- Code for a SABR-type model, when learning the volatility of the price dynamics. (Stoch_vol_regressionQV_SABR.py)
- Code for a Heston model generated implied volatility surface with constant model parameters.(MC_Heston.ipynb)
- Code for market-data with constant parameters.(MC_market_calibration.ipynb)
- Code for a market-data with time-varying parameters. (Cluster_MC_Time_Varying.py ran on the UniWien HPC3 Cluster and the corresponding notebook to visualize the results Calibration_TimeVarying.ipynb)
- Code for a Heston model with constant parameters. (Joint_Calibration.py)
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