On calibration methods for some financial models driven by non-Gaussian noises

摘要

In this paper we study the calibration problems arising in the fitting of non-gaussian models to financial data. We consider jump-diffusion models in the continuous time framework and Autoregressive Moving Average process (ARMA) with stable noises in the discrete time setting. We discuss asymptotic properties of the estimators and numerical implementation of the methods.