MicrostructureNoise is a Julia package for Bayesian volatility estimation in presence of market microstructure noise.
To install, run:
Pkg.add("MicrostructureNoise")
MicrostructureNoise estimates the volatility function of the stochastic differential equation
from noisy observations of its solution
where denote unobservable stochastic disturbances. The method is minimalistic in its assumptions on the volatility function, which in particular can itself be a stochastic process.
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 . This relies on the Kalman filter. The unknown squared volatility function is a priori modelled as piecewise constant and is assigned the inverse Gamma Markov chain prior, which induces smoothing among adjacent pieces of the function. The picture below gives an idea of the results obtainable with the method. Depicted is a reconstruction of the volatility function from the synthetic data generated according to the classical Heston stochastic volatility model (the unobserved true volatility curve is plotted in red). Note that next to the point estimate (posterior mean plotted in black), the method conducts automatic uncertainty quantification via the marginal Bayesian credible band (plotted in blue).
When is observed without noise, an option (
fixeta) allows to perfom inference as described in the reference "Nonparametric Bayesian volatility estimation".
See https://mschauer.github.io/MicrostructureNoise.jl/latest.
See issue #1 (Roadmap/Contribution) for questions and coordination of the development.
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Shota Gugushvili, Frank van der Meulen, Moritz Schauer, and Peter Spreij: Nonparametric Bayesian volatility estimation. In: de Gier, J., Praeger, C. E., and Tao, T. (editors), 2017 MATRIX Annals, pages 279–302, Cham, 2019. Springer International Publishing. doi: 10.1007/978-3-030-04161-8_19
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Shota Gugushvili, Frank van der Meulen, Moritz Schauer, and Peter Spreij: Nonparametric Bayesian volatility learning under microstructure noise. Jpn. J. Stat. Data Sci., 6: 551–571, 2023. doi: 10.1007/s42081-022-00185-9
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A. Taylan Cemgil and Onur Dikmen: Conjugate gamma Markov random fields for modelling nonstationary sources. In: Davies, M. E., James, C. J., Abdallah, S. A., Plumbley, M. D. (editors), Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. doi: 10.1007/978-3-540-74494-8_87