Calibrating the Mean-Reversion Parameter in the Hull-White Model Using Neural Networks

  • Georgios MoysiadisEmail author
  • Ioannis Anagnostou
  • Drona Kandhai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11054)


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.


Neural networks Time-dependent mean-reversion Calibration Interest rate models Hull-White model 



This project has received funding from Sofoklis Achilopoulos foundation ( and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement no. 675044 (, Training for Big Data in Financial Research and Risk Management.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Georgios Moysiadis
    • 1
    Email author
  • Ioannis Anagnostou
    • 1
    • 2
  • Drona Kandhai
    • 1
    • 2
  1. 1.Quantitative AnalyticsING BankAmsterdamNetherlands
  2. 2.Computational Science LabUniversity of AmsterdamAmsterdamNetherlands

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