mschauer/MicrostructureNoise.jl: v0.11.0

MicrostructureNoise is a Julia package for Bayesian volatility estimation in presence of market microstructure noise. The estimation methodology is intuitive to understand, given that its ingredients are well-known statistical techniques. The posterior inference is performed via the Gibbs sampler, with the Forward Filtering Backward Simulation algorithm used to reconstruct unobservable states.

See https://mschauer.github.io/MicrostructureNoise.jl/latest for the documentation.

References

• Shota Gugushvili, Frank van der Meulen, Moritz Schauer, and Peter Spreij: Nonparametric Bayesian volatility estimation. arxiv:1801.09956, 2018.

• Shota Gugushvili, Frank van der Meulen, Moritz Schauer, and Peter Spreij: Nonparametric Bayesian volatility learning under microstructure noise. In preparation.