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MicrostructureNoise

Moritz Schauer; Shota Gugushvili

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.

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