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Nonparametric Mixtures of Multi-Output Heteroscedastic Gaussian Processes for Volatility Modeling

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Emmanouil A. Platanios

Sotirios P. Chatzis


In this work, we present a nonparametric Bayesian method for multivariate volatility modeling. Our approach is based on postulation of a novel mixture of multi-output heteroscedastic Gaussian processes to model the covariance matrices of multiple assets. Specifically, we use the Pitman-Yor process prior as the non-parametric prior imposed over the components of our model, which are taken as multi-output heteroscedastic Gaussian processes obtained by introducing appropriate convolution kernels that combine simple heteroscedastic Gaussian processes under a multi-output scheme. We exhibit the efficacy of our approach in a volatility prediction task.