A neural network-based framework for financial model calibration
File(s)
Published version
Author(s)
Liu, Shuaiqiang
Borovykh, Anastasia
Grzelak, Lech A
Oosterlee, Cornelis W
Type
Journal Article
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.
Date Issued
2019-09-05
Date Acceptance
2019-08-29
Citation
Journal of Mathematics in Industry, 2019, 9 (1)
ISSN
2190-5983
Publisher
Springer
Journal / Book Title
Journal of Mathematics in Industry
Volume
9
Issue
1
Copyright Statement
© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000484579600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Physical Sciences
Mathematics, Interdisciplinary Applications
Mathematics
Computational finance
Machine learning
Artificial neural networks
Asset pricing model
Model calibration
Global optimization
Parallel computing
STOCHASTIC VOLATILITY
IMPLIED VOLATILITY
INVERSE PROBLEM
APPROXIMATION
OPTIONS
BOUNDS
Publication Status
Published
Article Number
ARTN 9
Date Publish Online
2019-09-05