CSpace  > 系统科学研究所
Wang Ximei1; He Xingkang1; Bao Ying2; Zhao Yanlong1
Source Publicationsciencechinainformationscience
AbstractHeston model is the most famous stochastic volatility model in finance. This paper considers the parameter estimation problem of Heston model with both known and unknown volatilities. First, parameters in equity process and volatility process of Heston model are estimated separately since there is no explicit solution for the likelihood function with all parameters. Second, the normal maximum likelihood estimation(NMLE) algorithm is proposed based on the Ito transformation of Heston model. The algorithm can reduce the estimate error compared with existing pseudo maximum likelihood estimation. Third, the NMLE algorithm and consistent extended Kalman filter(CEKF) algorithm are combined in the case of unknown volatilities. As an advantage, CEKF algorithm can apply an upper bound of the error covariance matrix to ensure the volatilities estimation errors to be well evaluated. Numerical simulations illustrate that the proposed NMLE algorithm works more efficiently than the existing pseudo MLE algorithm with known and unknown volatilities. Therefore, the upper bound of the error covariance is illustrated. Additionally, the proposed estimation method is applied to American stock market index S&P 500, and the result shows the utility and effectiveness of the NMLE-CEKF algorithm.
Document Type期刊论文
2.Risk Management Department, Industrial and Commercial Bank of China
Recommended Citation
GB/T 7714
Wang Ximei,He Xingkang,Bao Ying,et al. parameterestimatesofhestonstochasticvolatilitymodelwithmleandconsistentekfalgorithm[J]. sciencechinainformationscience,2018,61(4):17.
APA Wang Ximei,He Xingkang,Bao Ying,&Zhao Yanlong.(2018).parameterestimatesofhestonstochasticvolatilitymodelwithmleandconsistentekfalgorithm.sciencechinainformationscience,61(4),17.
MLA Wang Ximei,et al."parameterestimatesofhestonstochasticvolatilitymodelwithmleandconsistentekfalgorithm".sciencechinainformationscience 61.4(2018):17.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang Ximei]'s Articles
[He Xingkang]'s Articles
[Bao Ying]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang Ximei]'s Articles
[He Xingkang]'s Articles
[Bao Ying]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang Ximei]'s Articles
[He Xingkang]'s Articles
[Bao Ying]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.