Nonparametric Bayesian volatility learning under microstructure noise

arXiv:1805.05606v2 Announce Type: replace-cross
Abstract: In this work, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time observations from a stochastic differential equation and develop a novel computational method to learn the diffusion coefficient of the equation. We take a nonparametric Bayesian approach, where we \emph{a priori} model the volatility function as piecewise constant. Its prior is specified via the inverse Gamma Markov chain. Sampling from the posterior is accomplished by …

abstract arxiv bayesian computational differential differential equation diffusion equation learn market noise novel q-fin.st stat.me stat.ml stochastic study type work