A neural network-based framework for financial model calibration
This work addresses the computation bottleneck in financial model calibration for practitioners, offering a fast and reliable technique, though it appears incremental as it builds on existing neural network and optimization methods.
The authors tackled the problem of calibrating financial asset price models by proposing CaNN, a neural network-based framework that efficiently and accurately calibrates parameters of high-dimensional stochastic volatility models.
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.