Deep Calibration of Local Stochastic Volatility models

Authors:

Christa Cuchiero (University of Vienna), Homepage

Wahid Khosrawi (ETH Zurich), Homepage

Josef Teichmann (ETH Zurich), Homepage

This is the repo corresponding to the paper:

A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models

For citations:
MDPI and ACS Style
Cuchiero, C.; Khosrawi, W.; Teichmann, J. A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models. Risks 2020, 8(4), 101.

@Article{cuchiero2020generative,
  author    = {Cuchiero, Christa and Khosrawi, Wahid and Teichmann, Josef},
  title     = {A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models},
  journal   = {Risks},
  year      = {2020},
  volume    = {8},
  number    = {4},
  pages     = {101},
  doi       = {10.3390/risks8040101},
  keywords  = {LSV calibration; neural SDEs; generative adversarial networks; deep hedging; variance reduction; stochastic optimization},
  publisher = {MDPI},
  url       = {https://www.mdpi.com/2227-9091/8/4/101},
}

Documentation

Will follow in the near future (this version requires tensorflow 1.13 and python 3)

If the environment is set up correctly, the following command should start all computations needed for the statistical test described in the paper:

user@host:~$ python3 stat_test.py

License

MIT License Copyright (c) 2020 Christa Cuchiero, Wahid Khosrawi, Josef Teichmann

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.