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.

关键词： jump-diffusion ARMA stable Value at risk maximum likelihood

作者: Pablo Olivares Alexander Alvarez

作者单位: Risklab Toronto,University of Toronto at Mississauga. Toronto,Canada University of Havana,Department of Applied Mathematics,Havana,Cuba

会议类型: 国际会议

会议名称: The First International Conference on Management Innovation(ICMI 2007)(管理创新会议)

会议地点: 上海

会议语种:英文

页码: 475-477

在线出版日期: 2007-06-04（万方平台首次上网日期，不代表论文的发表时间）