Interest rate models are widely used for simulations of interest rate movements and pricing of interest rate derivatives. We focus on the Hull-White model, for which we develop a technique for calibrating the speed of mean reversion. We examine the theoretical time-dependent version of mean reversion function and propose a neural network approach to perform the calibration based solely on historical interest rate data. The experiments indicate the suitability of depth-wise convolution and provide evidence for the advantages of neural network approach over existing methodologies. The proposed models produce mean reversion comparable to rolling-window linear regression’s results, allowing for greater flexibility while being less sensitive to market turbulence.
CITATION STYLE
Moysiadis, G., Anagnostou, I., & Kandhai, D. (2019). Calibrating the mean-reversion parameter in the hull-white model using neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11054 LNAI, pp. 23–36). Springer Verlag. https://doi.org/10.1007/978-3-030-13463-1_2
Mendeley helps you to discover research relevant for your work.