Efficient Parameter Estimation for Multivariate Jump-Diffusions

This paper develops unbiased estimators of the transition density and posterior filters of a multivariate jump-diffusion process. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and... [ view full abstract ]

This paper develops unbiased estimators of the transition density and posterior filters of a multivariate jump-diffusion process. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. It is not nec- essary to diagonalize the volatility matrix. Our approach facilitates the parametric estimation of multivariate jump-diffusion models based on discretely observed data. Our parameter estimators have the same asymptotic behavior as maximum like- lihood estimators under mild conditions. Our methodology is found to be highly accurate and computationally efficient for the estimation of consumption growth dynamics.