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題名 Hedging Derivative Securities with Genetic Programming
作者 陳樹衡;W.-C. Lee;C.-H. Yeh
Chen,Shu-Heng;Lee,Wo-Chiang ;Yeh,Chia-Hsuan
關鍵詞 option pricing;Black-Scholes model;genetic programming;tracking error
日期 1999-12
上傳時間 9-Jan-2009 12:15:22 (UTC+8)
摘要 One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulas. Earlier studies take the Black–Scholes model as the true model and use the artificial data generated by it to train and to test GP. The aim of this paper is to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test our GP by distinguishing the case in-the-money from the case out-of-the-money. Unlike most empirical studies, we do not evaluate the performance of GP in terms of its pricing accuracy. Instead, the derived GP tree is compared with the Black–Scholes model in its capability to hedge. To do so, a notion of tracking error is taken as the performance measure. Based on the post-sample performance, it is found that in approximately 20% of the 97 test paths GP has a lower tracking error than the Black–Scholes formula. We further compare our result with the ones obtained by radial basis functions and multilayer perceptrons and one-stage GP. Copyright © 1999 John Wiley & Sons, Ltd.
關聯 International Journal of Intelligent Systems in Accounting Finance and Management,4(8),237-251
資料類型 article
DOI http://dx.doi.org/10.1002/(SICI)1099-1174(199912)8:4<237::AID-ISAF174>3.0.CO;2-J
dc.creator (作者) 陳樹衡;W.-C. Lee;C.-H. Yehzh_TW
dc.creator (作者) Chen,Shu-Heng;Lee,Wo-Chiang ;Yeh,Chia-Hsuan-
dc.date (日期) 1999-12en_US
dc.date.accessioned 9-Jan-2009 12:15:22 (UTC+8)-
dc.date.available 9-Jan-2009 12:15:22 (UTC+8)-
dc.date.issued (上傳時間) 9-Jan-2009 12:15:22 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/23255-
dc.description.abstract (摘要) One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulas. Earlier studies take the Black–Scholes model as the true model and use the artificial data generated by it to train and to test GP. The aim of this paper is to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test our GP by distinguishing the case in-the-money from the case out-of-the-money. Unlike most empirical studies, we do not evaluate the performance of GP in terms of its pricing accuracy. Instead, the derived GP tree is compared with the Black–Scholes model in its capability to hedge. To do so, a notion of tracking error is taken as the performance measure. Based on the post-sample performance, it is found that in approximately 20% of the 97 test paths GP has a lower tracking error than the Black–Scholes formula. We further compare our result with the ones obtained by radial basis functions and multilayer perceptrons and one-stage GP. Copyright © 1999 John Wiley & Sons, Ltd.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) International Journal of Intelligent Systems in Accounting Finance and Management,4(8),237-251en_US
dc.subject (關鍵詞) option pricing;Black-Scholes model;genetic programming;tracking error-
dc.title (題名) Hedging Derivative Securities with Genetic Programmingen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1002/(SICI)1099-1174(199912)8:4<237::AID-ISAF174>3.0.CO;2-Jen_US
dc.doi.uri (DOI) http://dx.doi.org/10.1002/(SICI)1099-1174(199912)8:4<237::AID-ISAF174>3.0.CO;2-Jen_US