Inference for Diffusion Processes using Combined Estimating Functions
Abstract
A class of martingale estimating functions provides a convenient framework for studying inference for nonlinear time series models. Further, when information about higher order conditional moments of the observed process is available, the estimation based on combined estimating functions becomes more informative. In this paper, a general framework is developed for estimating parameters of diffusion processes with discretely sampled data using combined estimating functions. The approach is used to study parameter estimation for diffusion models for asset pricing including the Black Scholes model, the Vasicek model, and the Cox-Ingersoll-Ross (CIR) model. Closed form expressions for the gain in information are also discussed in some detail.
DOI: http://dx.doi.org/10.4038/sljastats.v12i0.4972
Sri Lankan Journal of Applied Statistics Vol.12 2011 pp.145-160
© 2012 A Thavaneswaran, You Liang, N Ravishanker, published by The Institute of Applied Statistics, Sri Lanka
This work is licensed under the Creative Commons License.