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Interest Rate Model Calibration using Gaussian Processes for Machine Learning

With Kernel Machines playing a central role in Machine Learning, models based on Gaussian Processes have become increasely popular for problems of regression and classification.
A problem of learning with Gaussian Processes is the specification of mean and covariance functions to use. This requires detailed prior information on the data which in many problems is not known.
In the field of Financial Mathematical Modeling some Gaussian processes are extensively used because they proved to be good models for some prices sequences, and also because of their analytical simplicity. Their mean and covariance functions are analytically computable.
A problem of using such models is the calibration problem. What parameters to use given historical and present prices.
In this talk we connect the worlds of Machine Learning and Financial Mathematical Modeling.
We calibrate (learn the parameters) of a Gaussian interest rate model (the Vasicek model) with Gaussian Processes for Machine Learning using the mean and covariance functions computed analytically.