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| Title | Option pricing with genetic algorithms: a second report |
| Creator | Chen, Shu-heng;Lee, Woh-Chiang 陳樹衡 |
| Contributor | 經濟系 |
| Date | 1997 |
| Date Issued | 11-May-2015 14:50:33 (UTC+8) |
| Summary | 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 |
| Relation | International Symposium on Neural Networks - ISNN 21 - 25 vol.1 |
| Type | 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 |