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Machine Learning Vasicek Model Calibration with Gaussian Processes

Sousa, J. Beleza ; Esquível, M. L. ; Gaspar, R. M.

Communications in statistics. Simulation and computation, 2012-07, Vol.41 (6), p.776-786 [Periódico revisado por pares]

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Título:
Machine Learning Vasicek Model Calibration with Gaussian Processes
Autor: Sousa, J. Beleza ; Esquível, M. L. ; Gaspar, R. M.
Assuntos: Arbitrage free risk neutral measure ; Calibration ; Gaussian processes for machine learning ; Interest rates ; Primary 91G80 ; Secondary 91G30, 60G15, 68T05 ; Studies ; Vasicek interest rate model ; Zero coupon bond prices ; Zero coupon bonds
É parte de: Communications in statistics. Simulation and computation, 2012-07, Vol.41 (6), p.776-786
Descrição: In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.
Editor: Colchester: Taylor & Francis Group
Idioma: Inglês;Russo

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