Moritz Schauer
Shota Gugushvili
2020-02-13
<p>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.</p>
<p>See <a href="https://mschauer.github.io/MicrostructureNoise.jl/latest">https://mschauer.github.io/MicrostructureNoise.jl/latest</a> for the documentation.</p>
<p>References</p>
<ul>
<li>
<p>Shota Gugushvili, Frank van der Meulen, Moritz Schauer, and Peter Spreij: Nonparametric Bayesian volatility estimation. <a href="https://arxiv.org/abs/1801.09956">arxiv:1801.09956</a>, 2018.</p>
</li>
<li>
<p>Shota Gugushvili, Frank van der Meulen, Moritz Schauer, and Peter Spreij: Nonparametric Bayesian volatility learning under microstructure noise. In preparation.</p>
</li>
</ul>
https://doi.org/10.5281/zenodo.3666658
oai:zenodo.org:3666658
Zenodo
https://github.com/mschauer/MicrostructureNoise.jl/tree/v0.11.0
https://doi.org/10.5281/zenodo.1241010
info:eu-repo/semantics/openAccess
Other (Open)
mschauer/MicrostructureNoise.jl: v0.11.0
info:eu-repo/semantics/other