A neural network-based framework for financial model calibration

Shuaiqiang Liu*, Anastasia Borovykh, Lech Grzelak, Cornelis W. Oosterlee

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

29 Citations (Scopus)
164 Downloads (Pure)


A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The framework consists of two parts: a forward pass in which we train the weights of the ANN off-line, valuing options under many different asset model parameter settings; and a backward pass, in which we evaluate the trained ANN-solver on-line, aiming to find the weights of the neurons in the input layer. The rapid on-line learning of implied volatility by ANNs, in combination with the use of an adapted parallel global optimization method, tackles the computation bottleneck and provides a fast and reliable technique for calibrating model parameters while avoiding, as much as possible, getting stuck in local minima. Numerical experiments confirm that this machine-learning framework can be employed to calibrate parameters of high-dimensional stochastic volatility models efficiently and accurately.

Original languageEnglish
Article number9
Pages (from-to)1-28
Number of pages28
JournalMathematics in Industry
Issue number1
Publication statusPublished - 2019


  • Artificial neural networks
  • Asset pricing model
  • Computational finance
  • Global optimization
  • Machine learning
  • Model calibration
  • Parallel computing


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