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題名 Option pricing with genetic algorithms: a second report 作者 Chen, Shu-heng;Lee, Woh-Chiang
陳樹衡貢獻者 經濟系 日期 1997 上傳時間 11-May-2015 14:50:33 (UTC+8) 摘要 The cross-fertilization between artificial intelligence and computational finance has resulted in some of the most active research areas in financial engineering. One direction is the application of machine learning techniques to pricing financial products, which is certainly one of the most complex issues in finance. In the literature, when the interest rate, the mean rate of return and the volatility of the underlying asset follow general stochastic processes, the analytical solution is usually not available. Over the last two years, artificial neural nets have been applied to solve option pricing numerically. However, so far, there is no applications based on evolutionary computation in this area. In this paper, we illustrate how genetic algorithms (GAs), as an alternative to neural nets, can be potentially helpful in dealing with option pricing. In particular, we test the performance of basic genetic algorithms by applying them to the determination, of prices of European call options, whose exact solution is known from Black-Scholes option pricing theory. The solutions found by basic genetic algorithms are compared with the exact solution, and the performance of GAs is evaluated accordingly 關聯 International Symposium on Neural Networks - ISNN 21 - 25 vol.1 資料類型 conference DOI http://dx.doi.org/10.1109/ICNN.1997.611628 dc.contributor 經濟系 dc.creator (作者) Chen, Shu-heng;Lee, Woh-Chiang dc.creator (作者) 陳樹衡 zh_TW dc.date (日期) 1997 dc.date.accessioned 11-May-2015 14:50:33 (UTC+8) - dc.date.available 11-May-2015 14:50:33 (UTC+8) - dc.date.issued (上傳時間) 11-May-2015 14:50:33 (UTC+8) - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75073 - dc.description.abstract (摘要) The cross-fertilization between artificial intelligence and computational finance has resulted in some of the most active research areas in financial engineering. One direction is the application of machine learning techniques to pricing financial products, which is certainly one of the most complex issues in finance. In the literature, when the interest rate, the mean rate of return and the volatility of the underlying asset follow general stochastic processes, the analytical solution is usually not available. Over the last two years, artificial neural nets have been applied to solve option pricing numerically. However, so far, there is no applications based on evolutionary computation in this area. In this paper, we illustrate how genetic algorithms (GAs), as an alternative to neural nets, can be potentially helpful in dealing with option pricing. In particular, we test the performance of basic genetic algorithms by applying them to the determination, of prices of European call options, whose exact solution is known from Black-Scholes option pricing theory. The solutions found by basic genetic algorithms are compared with the exact solution, and the performance of GAs is evaluated accordingly dc.format.extent 129 bytes - dc.format.mimetype text/html - dc.relation (關聯) International Symposium on Neural Networks - ISNN 21 - 25 vol.1 dc.title (題名) Option pricing with genetic algorithms: a second report dc.type (資料類型) conference en dc.identifier.doi (DOI) 10.1109/ICNN.1997.611628 dc.doi.uri (DOI) http://dx.doi.org/10.1109/ICNN.1997.611628
