Neural Networks for Derivatives Pricing & Calibration Models

“Make the model go faster” solutions cannot keep pace.

Typically, derivatives risk management groups within financial institutions run a multitude of risk scenarios that entail thousands of risk factors on huge derivatives portfolios. With millions of risk calculations, financial organizations are confronted with a daunting computational challenge.

Given that many financial derivatives are priced under complex volatility models, for which accurate analytic formulae are not available and which require slow multi-factor partial differential equations (PDEs) or Monte Carlo pricing methods, a new risk management pricing model paradigm is required.

Neural Networks can be used as universal function approximators significantly accelerating the valuation of financial derivatives. After training on a data set generated by a rigorous, computationally intensive financial model, the trained Neural Networks can approximate the model’s results in a highly efficient manner.  Computation times may be reduced by orders of magnitude, while preserving fidelity to the rigorous model results.

SciComp, a leading provider of derivatives pricing and risk management solutions, develops Neural Network solutions for sophisticated derivatives pricing and calibration models.