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.
Operations Research Colloquia: http://or.stanford.edu/oras_seminars.html