Efficient Parameter Estimation for Multivariate Jump-Diffusions


Gustavo Schwenkler
Boston University

Monday, October 26, 2015
3:10 - 4:10 PM
Location: Hewlett 103


Abstract:

Our paper develops an unbiased and computationally efficient Monte-Carlo estimator of the transition density of a multivariate jump-diffusion process. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. Most importantly, it is not necessary that the variance-covariance matrix can be diagonalized using a change of variable or change of time. Our density estimator facilitates the parametric estimation of multivariate jump diffusion models based on low frequency data. The parameter estimators we propose have the same asymptotic behavior as maximum likelihood estimators under mild conditions that can be verified using our density estimator. Numerical case studies illustrate our results. This is joint work with François Guay.




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