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:1.3872

@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",
  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...",
}

Genetic Programming entries for Shu-Heng Chen Chia Hsuan Yeh

Citations