文献类型：期刊文献

英文题名：Entropy binomial tree method and calibration for the volatility smile

作者：Gong, Wenxiu[1];Xu, Zuoliang[1];Ma, Qinghua[2]

第一作者：Gong, Wenxiu

通讯作者：Xu, ZL[1]

机构：[1]Renmin Univ China, Sch Math, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Appl Arts & Sci, Beijing, Peoples R China

第一机构：Renmin Univ China, Sch Math, Beijing, Peoples R China

通讯机构：[1]corresponding author), Renmin Univ China, Sch Math, Beijing, Peoples R China.

年份：2020

卷号：28

期号：11

起止页码：1591-1608

外文期刊名：INVERSE PROBLEMS IN SCIENCE AND ENGINEERING

收录：;EI(收录号：20202108692966);Scopus(收录号：2-s2.0-85084992111);WOS:【SSCI(收录号:WOS:000532549800001)，SCI-EXPANDED(收录号:WOS:000532549800001)】；

基金：The work is supported by National Natural Science Foundation of China [grant number 11571365].

语种：英文

外文关键词：Entropy binomial tree; volatility; option pricing; calibration; nonlinear constrained optimization

摘要：In this paper, we combine the maximum entropy principle with binomial tree to construct a non-recombining entropy binomial tree pricing model under the volatility that is a function of time, and give the rate of convergence. The model may yield an unbiased and objective probability. In addition, we research the calibration problem of volatility with the entropy binomial tree, and adopt an exterior penalty method to transform this problem into a nonlinear unconstrained optimization problem. For the optimization problem, we use the quasi-Newton algorithm. Finally, we test our model by numerical examples and options data on the S&P 500 index. The results confirm the effectiveness of the entropy binomial tree pricing model.