Using Genetic Programming to Model Volatility in Financial Time Series: The Case of Nikkei 225 and S\&P 500

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{chen:1997:GPmvfts:NS+P,

• author = "Shu-Heng Chen and Chia-Hsuan Yeh",
• title = "Using Genetic Programming to Model Volatility in Financial Time Series: The Case of {Nikkei 225} and {S\&P 500}",
• booktitle = "Proceedings of the 4th JAFEE International Conference on Investments and Derivatives (JIC'97)",
• year = "1997",
• pages = "288--306",
• address = "Aoyoma Gakuin University, Tokyo, Japan",
• month = jul # " 29-31",
• keywords = "genetic algorithms, genetic programming, recursive genetic programming, structural changes, model-specific structural changes, model-free structural changes, improvement sequence",
• URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/JIC97/jic97.ps",
• URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1997zSzJIC97zSzjic97.pdf/chen97using.pdf",
• URL = "http://citeseer.ist.psu.edu/322892.html",
• size = "16 pages",
• abstract = "In this paper we propose a time-variant and non-parametric approach to estimating volatility. This approach is based on recursive genetic programming (RGP). Here, volatility is estimated by a class of non-parametric models which are generated through a recursive competitive process. The essential feature of this approach is that it can estimate volatility by simultaneously detecting and adapting to structural changes. Thus, volatility is estimated by taking possible structural changes into account. When RGP discovers structural changes, it will quickly suggest a new class of models so that overestimation of volatility due to ignorance of structural changes can be avoided. The idea of this work is motivated by two lines of research in two different fields; one is the volatility estimation through articial neural nets in nancial engineering, and the other the design of robust adaptive systems under uncertain circumstances in articial intelligence. In this paper, the idea is ...",
• notes = "https://ci.nii.ac.jp/ncid/AA12420031",

}

Genetic Programming entries for Shu-Heng Chen Chia Hsuan Yeh

Citations