文章基本信息

标题：Numerical Simulation of the Heston Model under Stochastic Correlation
本地全文：下载
作者：Teng, Long ; Ehrhardt, Matthias ; Günther, Michael 等
期刊名称：International Journal of Financial Studies
印刷版ISSN：2227-7072
出版年度：2017
卷号：6
期号：1
页码：1-16
出版社：MDPI, Open Access Journal
摘要：Stochastic correlation models have become increasingly important in financial markets. In order to be able to price vanilla options in stochastic volatility and correlation models, in this work, we study the extension of the Heston model by imposing stochastic correlations driven by a stochastic differential equation. We discuss the efficient algorithms for the extended Heston model by incorporating stochastic correlations. Our numerical experiments show that the proposed algorithms can efficiently provide highly accurate results for the extended Heston by including stochastic correlations. By investigating the effect of stochastic correlations on the implied volatility, we find that the performance of the Heston model can be proved by including stochastic correlations.
关键词：Heston model; stochastic correlation process; Ornstein-Uhlenbeck process; quadratic-exponential scheme