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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)

Abstract

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.

Keywords

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

Notes

Acknowledgment

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

References

  1. 1.
    BIS: Over-the-counter derivatives statistics. https://www.bis.org/statistics/derstats.htm. Accessed 05 Feb 2018
  2. 2.
    Hull, J., White, A.: Pricing interest-rate-derivative securities. Rev. Financ. Stud. 3(4), 573–592 (1990)CrossRefGoogle Scholar
  3. 3.
    Suarez, E.D., Aminian, F., Aminian, M.: The use of neural networks for modeling nonlinear mean reversion: measuring efficiency and integration in ADR markets. IEEE (2012)Google Scholar
  4. 4.
    Zapranis, A., Alexandridis, A.: Weather derivatives pricing: modeling the seasonal residual variance of an Ornstein-Uhlenbeck temperature process with neural networks. Neurocomputing 73, 37–48 (2009)CrossRefGoogle Scholar
  5. 5.
    Vasicek, O.: An equilibrium characterization of the term structure. J. Financ. Econ. 5(2), 177–188 (1977)CrossRefGoogle Scholar
  6. 6.
    Hull, J.: Options, Futures, and Other Derivatives. Pearson/Prentice Hall, Upper Saddle River (2006)zbMATHGoogle Scholar
  7. 7.
    Exley, J., Mehta, S., Smith, A.: Mean reversion. In: Finance and Investment Conference, pp. 1–31. Citeseer (2004)Google Scholar
  8. 8.
    Narayanan, H., Mitter, S.: Sample complexity of testing the manifold hypothesis. In: Advances in Neural Information Processing Systems, vol. 23, Curran Associates Inc., Red Hook (2010)Google Scholar
  9. 9.
    Wei, L.-Y., Cheng, C.-H.: A hybrid recurrent neural networks model based on synthesis features to forecast the Taiwan stock market. Int. J. Innov. Comput. Inf. Control 8(8), 5559–5571 (2012)Google Scholar
  10. 10.
    Hernandez, A.: Model calibration with neural networks. Risk.net, July 2016Google Scholar
  11. 11.
    Gurrieri, S., Nakabayashi, M., Wong, T.: Calibration methods of Hull-White model, November 2009.  https://doi.org/10.2139/ssrn.1514192
  12. 12.
    Sepp, A.: Numerical implementation of Hull-White interest rate model: Hull-white tree vs finite differences. Technical report, Working Paper, Faculty of Mathematics and Computer Science, Institute of Mathematical Statistics, University of Tartu (2002)Google Scholar
  13. 13.
    Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., Iosifidis, A.: Forecasting stock prices from the limit order book using convolutional neural networks. IEEE (2017)Google Scholar
  14. 14.
    Luo, R., Zhang, W., Xu, X., Wang, J.: A neural stochastic volatility model. arXiv preprint arXiv:1712.00504 (2017)
  15. 15.
    Galeshchuk, S., Mukherjee, S.: Deep networks for predicting direction of change in foreign exchange rates. Intell. Syst. Acc. Financ. Manag. 24(4), 100–110 (2017)CrossRefGoogle Scholar
  16. 16.
    Zapranis, A., Alexandridis, A.: Modelling the temperature time-dependent speed of mean reversion in the context of weather derivatives pricing. Appl. Math. Financ. 15(4), 355–386 (2008)MathSciNetCrossRefGoogle Scholar
  17. 17.
    LeCun, Y., Touresky, D., Hinton, G., Sejnowski, T.: A theoretical framework for back-propagation. In: Proceedings of the 1988 Connectionist Models Summer School (1988)Google Scholar
  18. 18.
    El Kolei, S., Patras, F.: Analysis, detection and correction of misspecified discrete time state space models. J. Comput. Appl. Math. 333, 200–214 (2018)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Shwartz-Ziv, R., Tishby, N.: Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810 (2017)

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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