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Register a new userNonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations
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Added by
Moritz Schauer
Publication date
2020
fields
Mathematical Statistics
and research's language is
English
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We study a nonparametric Bayesian approach to estimation of the volatility function of a stochastic differential equation driven by a gamma process. The volatility function is modelled a priori as piecewise constant, and we specify a gamma prior on its values. This leads to a straightforward procedure for posterior inference via an MCMC procedure. We give theoretical performance guarantees (contraction rates for the posterior) for the Bayesian estimate in terms of the regularity of the unknown volatility function. We illustrate the method on synthetic and real data examples.
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