Calibrating the Mean-Reversion Parameter in the Hull-White Model Using Neural Networks
| Autor: | Moysiadis, G., Anagnostou, I., Kandhai, D., Alzate, C., Monreale, A. |
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| Přispěvatelé: | Computational Science Lab (IVI, FNWI) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
040101 forestry
050208 finance Artificial neural network Interest rate derivative Computer science media_common.quotation_subject 05 social sciences 04 agricultural and veterinary sciences Function (mathematics) Hull–White model Convolution Interest rate 0502 economics and business Linear regression Mean reversion 0401 agriculture forestry and fisheries Algorithm media_common |
| Zdroj: | Lecture Notes in Computer Science Lecture Notes in Computer Science-ECML PKDD 2018 Workshops ECML PKDD 2018 Workshops-MIDAS 2018 and PAP 2018, Dublin, Ireland, September 10-14, 2018, Proceedings ECML PKDD 2018 Workshops: MIDAS 2018 and PAP 2018, Dublin, Ireland, September 10-14, 2018 : proceedings, 23-36 STARTPAGE=23;ENDPAGE=36;TITLE=ECML PKDD 2018 Workshops ECML PKDD 2018 Workshops ISBN: 9783030134624 |
| ISSN: | 0302-9743 1611-3349 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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