Christa Cuchiero (University of Vienna) on "Calibration of financial models with neural networks"

A central task in modeling, which has to be performed each day in banks and financial institutions, is to calibrate models to market and historical data. So far the choice which models should be used was not only driven by their capacity of
capturing empirically observed market features well, but rather by computational tractability considerations. This is now undergoing a big change since neural network approaches offer the possibility to transform a daily online calibration into an offline learning phase and an online evaluation phase where the latter will be - thanks to the learning phase - extremely fast no matter what complex type of model needs to be calibrated. Inspired by the work of Andrez Hernandez [2], we consider two examples of calibration with neural networks: first a mixture model for interest rate dynamics in the spirit of [1] and second a local stochastic volatility model where the local volatility function is parametrized via neural nets.

The talk is based on joint work with Andres Hernandez, Wahid Khosrawi and
Josef Teichmann.

 

[1] D. Brigo and F. Mercurio. Lognormal-mixture dynamics and calibration to market volatility smiles. International Journal of Theoretical and Applied Finance, 5(4):427-446, 2002.

[2] A. Hernandez. Model calibration with neural networks.
https://papers.ssrn.com/sol3/papers.cfm?abstract id=2812140, 2016.