A Dirichlet-process-plus-Gamma construction enables MCMC inference of jump measures for subordinator-driven state-space models.
Spectral-free estimation of L\'evy densities in high-frequency regime
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abstract
We construct an estimator of the L\'evy density of a pure jump L\'evy process, possibly of infinite variation, from the discrete observation of one trajectory at high frequency. The novelty of our procedure is that we directly estimate the L\'evy density relying on a pathwise strategy, whereas existing procedures rely on spectral techniques. By taking advantage of a compound Poisson approximation, we circumvent the use of spectral techniques and in particular of the L\'evy--Khintchine formula. A linear wavelet estimator is built and its performance is studied in terms of $L_p$ loss functions, $p\geq 1$, over Besov balls. We recover classical nonparametric rates for finite variation L\'evy processes and for a large nonparametric class of symmetric infinite variation L\'evy processes. We show that the procedure is robust when the estimation set gets close to the critical value 0 and also discuss its robustness to the presence of a Brownian part.
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Bayesian Non-Parametric Inference for L\'evy Measures in State-Space Models
A Dirichlet-process-plus-Gamma construction enables MCMC inference of jump measures for subordinator-driven state-space models.