Efficient estimation and filtering for multivariate jump–diffusions

François Guay and Gustavo Schwenkler

Journal of Econometrics, 2021, vol. 223, issue 1, 251-275

Abstract: This paper develops estimators of the transition density, filters, and parameters of multivariate jump–diffusion models. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. It is not necessary to diagonalize the volatility matrix. Our density and filter estimators converge at the canonical rate typically associated with exact Monte Carlo estimation. Our parameter estimators have the same asymptotic distribution as maximum likelihood estimators, which are often intractable for the class of models we consider. The results of this paper enable the empirical analysis of previously intractable models of asset prices and economic time series.

Keywords: Multivariate jump–diffusions; Likelihood inference; Filtering; Density estimation; Efficiency (search for similar items in EconPapers)
JEL-codes: C13 C32 C58 C63 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:223:y:2021:i:1:p:251-275

DOI: 10.1016/j.jeconom.2020.09.004

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