A neural network-based framework for financial model calibration

TL;DR

This paper introduces CaNN, a neural network framework that accelerates and improves the calibration of complex financial asset models using a two-phase training and evaluation process, enhancing speed and accuracy.

Contribution

The paper presents a novel neural network-based calibration method that combines offline training with online evaluation, reducing computational time and avoiding local minima in high-dimensional models.

Findings

Efficient calibration of high-dimensional stochastic volatility models.

Fast and reliable parameter estimation avoiding local minima.

Numerical experiments demonstrate high accuracy and speed.

Abstract

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.