basslv

This repository is a fork of https://github.com/igudav/Bass-Local-Volatility

Differences:

1. Added functionality to solve fixed-point equation for arbitrary tensors.
2. Completely redesigned the project architecture.
3. Add MarketMarginal class (correctly work for synthetic call prices from Black model)

Now work only for the Black-Scholes model.

Structure:

├── notebooks                                      <- examples in jupyter notebook
├── unittest                                       <- some tests
├── examples                                       <- examples
└── basslv   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes the basslv a Python module
    │
    ├── core
    │   ├── __init__.py
    │   ├── bassLocalVolatility.py                 <- contains the complete algorithm including Monte Carlo sampling
    │   ├── fixedPointEquation.py                  <- contains the numerical algorithm for building the mapping function
    │   ├── genericMarginal.py                     <- abstract class
    │   ├── heatKernelConvolutionEngine.py         <- contains methods to calculate convolution
    │   ├── logNormalMarginal.py                   <- exact case 
    │   ├── marketMarginal.py                      <- build marginal from market call prices
    │   ├── projectTyping.py                       <- auxiliary typing
    │   ├── solutionFixedPointEquation.py          <- realization of SolutionInterpolator  
    │   └── solutionInterpolator.py                <- abstract class - solution of fixed point equation
    │   
    └── visualVerification  
         ├── __init__.py
         └── visualVerification.py                 <- contains methods for visual verification

References:

1. Antoine Conze and Henry-Labordere, A new fast local volatility model:
   https://www.risk.net/media/download/1079736/download
2. Antoine Conze and Henry-Labordere, Bass Construction with Multi-Marginals:
   Lightspeed Computation in a New Local Volatility Model:
   https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3853085

Note. The references in the code are linked to the first article