Nonparametric Bayesian volatility learning under microstructure noise

1 code implementation • 15 May 2018 • Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij

In this work, we study the problem of learning the volatility under market microstructure noise.

Fast and scalable non-parametric Bayesian inference for Poisson point processes

4 code implementations • 10 Apr 2018 • Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij

The observations are assumed to be $n$ independent realisations of a Poisson point process on the interval $[0, T]$.

Methodology 62G20 (Primary) 62M30 (Secondary)

Nonparametric Bayesian volatility estimation

1 code implementation • 30 Jan 2018 • Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij

Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation.

Methodology Statistics Theory Statistical Finance Statistics Theory 62G20 (Primary), 62M05 (Secondary)