Estimating covariances between financial assets plays an important role in risk management and optimal portfolio allocation . In practice, when the sample size is small compared to the number of variables, i.e. when considering a wide universe of assets over just a few years, this poses considerable challenges and the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear extension of standard factor models with readily interpretable parameters reminiscent of market betas. Furthermore, our Bayesian treatment naturally shrinks the sample covariance matrix towards a more structured matrix given by the prior and thereby systematically reduces estimation errors  .
Estimating covariances between financial assets plays an important role in risk management . In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear extension of standard factor models with readily interpretable parameters reminiscent of market betas. Furthermore, our Bayesian treatment naturally shrinks the sample covariance matrix towards a more structured matrix given by the prior and thereby systematically reduces estimation errors . Finally, we discuss some financial applications of the GP-LVM .
D
and optimal portfolio allocation
D
i.e. when considering a wide universe of assets over just a few years, this poses considerable challenges and
A
. Finally, we discuss some financial applications of the GP-LVM