Bayesian inference for generalised tempered stable Levy processes.

广义调节稳定 Levy 过程的贝叶斯推理。

• 批准号: 406700014
• 负责人: Professor Dr. Denis Belomestny
• 金额: --
• 依托单位: Forschungsgebiet Mathematische Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/406700014?language=en
• 关键词: Bayesian; inference; generalised; tempered; stable

The goal of the project is the development of new efficient methods of Bayesian inference for Levy processes based on their discrete-time observations and theoretical investigation of these methods. In particular, for the class of generalized tempered stable processes, we plan to estimate the tempering function using a nonparametric Bayesian approach. An important task is development of efficient MCMC algorithms and proof of the corresponding contraction rates. The implementation of proposed methods and their application to financial and insurance data is also foreseen.

该项目的目标是开发新的有效的方法的贝叶斯推理的利维过程的基础上，他们的离散时间的观察和理论研究这些方法。特别是，对于广义回火稳定过程类，我们计划使用非参数贝叶斯方法估计回火函数。一个重要的任务是开发有效的MCMC算法和相应的收缩率的证明。还预计将实施拟议的方法并将其应用于金融和保险数据。