Neural Networks for Derivatives Pricing & Calibration Models | SciComp
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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.

Neural Network

Strategies for efficient Neural Network implementations may include:

  • Parametrizing training data sets to capture salient features in a parsimonious manner.
  • Employing existing analytic approximations or related formulae to further reduce the dynamic range of the training set.
  • Dividing global parameter space into regions, training separate networks for each.

SABR Examples

  • European futures option:
    • Train to implied volatility, deflated via approximate formula
    • Single hidden layer ANN, O(105) training data
    • RMS Implied vol and PV errors of 1bp, and 0.5bp respectively
    • 140,000 evaluations/sec
  • Double no-touch futures option:
    • Parameter space divided into regions based on estimated PV
    • Train to PV spread over Black-Scholes
    • Combined single hidden layer ANNs
    • RMS PV error of 3bp
    • 140,000 evaluations/sec

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