Least squares estimators for stochastic differential equations driven by small Levy noises

We study parameter estimation for discretely observed stochastic differential equations driven by small Levy noises. We do not impose Lipschitz condition on the dispersion coefficient function a and any moment condition on the driving Levy process, which greatly enhances the applicability of our results to many practical models. Under certain regularity conditions on the drift and dispersion functions, we obtain consistency and rate of convergence of the least squares estimator (LSE) of parameter when epsilon -> 0 and n -> infinity simultaneously. We present some simulation study on a two-factor financial model driven by stable noises. (c) 2016 Elsevier B.V. All rights reserved.