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以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：31 、訪客IP：3.135.202.38
姓名 陳侑德(Stanley Chen) 查詢紙本館藏 畢業系所 資訊管理學系 論文名稱 遺傳程式規劃於發展複製賣權策略之應用
(Rebuild the Synthetic Put Option by Using Genetic Programming)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 高報酬的投資工具往往也伴隨著高風險。在風險市場中如何保障投入的資產
價值並且能獲取不錯的利益是投資組合保險(Portfolio Insurance)研究的議題。
複製賣權策略(Synthetic Put Option)為投資組合保險常見的策略之一。此策略
透過連續調整資產分配在風險性資產與非風險性資產的部位，形成類似保護賣權
的資金結構，此結構能隨著市場上漲時獲取利潤，市場下跌時不跌過期初設定的
底值。然而一般複製賣權策略分配法則由Black-Scholes 選擇權評價模型推導。
此一方法為建構在假設下的模型驅動理論(model-driven approach)，無法配合環境
變動做適應性調整。
選擇權評價模型發展除了模型驅動理論外，另有一資料驅動理論(data-driven
approach)。此發展方式不需嚴格假設，透過機器學習技術(machine learning)以及
大量資料的訓練與學習，使發展出的模型具有跟隨資料變動做出適應性調整的彈
性。本研究即嘗試使用機器學習技術之一的遺傳程式規劃(Genetic Programming)
發展複製賣權策略。
實證結果發現，本系統架構所發展出的複製賣權策略能比Black-Scholes 模
型為基礎的策略更為接近保護賣權的結構，以達到保險的效果。另外在績效上也
有不錯的表現。儘管如此，但發展出的策略也還有可能跌破保險底值。摘要(英) There is lot of risk in the security market. How to protect one’s fortune from the risk is an important issue to investors. Portfolio insurance is one of the solutions to this question. It can help investors to gain the profit when the market is up and keep their portfolio equities when the market is down.
The synthetic put option (SPO) is one kind of portfolio insurance strategies. It provides the insurance by using stocks and money to synthesize options. But it’s a dynamic portfolio insurance, it needs to adjust two position continuously in order to match the structure of options.
Generally, most investors adjust the stock and money positions according to the Black-Scholes (B/S) option pricing model. But this way is model-driven approach. It has some defect. For example, model-driven approach can not adjust itself according to the change of environment. Data-driven approach is another way and it is more flexible. This research is want to rebuild the synthetic put option strategies by using genetic programming (GP) algorithm. GP is a kind of data-driven approach.
After experiments in this research, GP really can find out synthetic program that is better than B/S in matching the structure of option and return.關鍵字(中) ★ 投資組合保險
★ 遺傳程式規劃
★ 保護賣權
★ 複製賣權
★ 選擇權評價關鍵字(英) ★ option pricing
★ synthetic put option
★ genetic programming
★ protective put option
★ portfolio insurance論文目次 I
第一章、緒論..............................................................................................................1
第一節、研究背景與動機..................................................................................1
第二節、研究目的..............................................................................................2
第三節、研究步驟..............................................................................................2
第二章、文獻探討......................................................................................................4
第一節、選擇權..................................................................................................4
一、選擇權簡介..........................................................................................4
二、Black-Scholes 選擇權評價公式.........................................................5
三、選擇權複製..........................................................................................7
第二節、投資組合保險......................................................................................9
一、保護賣權策略......................................................................................9
二、複製賣權策略....................................................................................10
第三節、遺傳程式規劃....................................................................................12
第三章、運用遺傳程式規劃發展複製賣權策略....................................................18
第一節、複製賣權策略執行演算法................................................................18
第二節、複製賣權策略....................................................................................19
第三節、遺傳程式規劃架構............................................................................21
一、節點集合............................................................................................21
二、適應函數............................................................................................22
三、系統參數............................................................................................24
第四章、實驗結果與分析........................................................................................26
第一節、實驗設計............................................................................................26
第二節、實驗假設............................................................................................27
第三節、實驗一應用於台股加權指數..........................................................28
一、調整法則............................................................................................28
二、訓練期實驗結果................................................................................28
三、測試期實驗結果................................................................................32
第四節、實驗二應用於S&P500 指數..........................................................34
一、調整法則............................................................................................34
二、訓練期實驗結果................................................................................35
三、測試期實驗結果................................................................................38
第五節、實驗結果分析....................................................................................40
第五章、結論與建議................................................................................................42
第一節、研究結論............................................................................................42
II
第二節、研究貢獻............................................................................................42
第三節、後續研究方向....................................................................................43
參考文獻......................................................................................................................44參考文獻 [李沃牆，1998] 李沃牆，計算智慧在選擇權定價上的發展:人工神經網路、遺傳
程式規劃、遺傳演算法，政治大學經濟研究所博士論文，1998 年。
[柯淑玲，2000] 柯淑玲，運用類神經網路於台股認購權證評價模式之實證研究，
義守大學管理科學研究所碩士論文，2000 年。
[薛淑嫻，1996] 薛淑嫻，認購權證評價模型之研究- 基因演算法與類神經網路
之應用，銘傳大學金融研究所碩士論文，1998 年。
[賴彌煥，1999] 賴彌煥，權變投資組合保險在台灣股市之應用，成功大學企業
管理學系碩士論文，1999 年。
[Black and Scholes, 1973] Black, F. and M. Scholes, “The Pricing of Options and
Corporate Liabilities, ” Journal of Political Economy, 81, pp. 637-654.
[Freedman, 1996] Freedman, R.S. and R. D. Giorgio, “New Computational
Architecture for Pricing Derivatives,” Proceedings of the IEEE/IAFE 1996
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1996.
[Holland, 1975] Holland, J., Adaptation in Natural and Artificial Systems, University
of Michigan Press, 1975.
[Koza, 1992a] Koza, J.R., Genetic Programming: On the Programming of Computers
by Means of Natural Selection, MIT Press, 1992.
[Koza, 1992b] Koza, J.R. Genetic Programming II: Automatic Discovery of Reusable
Programs, MIT Press, 1992.b
[Leland, 1980] Leland, Hayne E., “Who Should Buy Portfolio Insurance,” Journal of
Finance, 35, pp.581, May 1980.
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Discontinuous,” Journal of Financial Economics, 3, pp125-135, 1979.
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Prices: Comment,” Journal of Finance, 28, pp.183-184, 1993.
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Economics and Management Science, 4, pp.141-184, 1973.
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[Rendleman, 1990] Rendleman, Richard J. and Thomas J. O’Brien, “The Effects of
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Analysts Journal, 46, pp61-71, May/Jun 1990.
[Rubinstein, 1981] Rubinstein, Mark and Hayne E. Leland, “Replicating Options with
Positions in Stock and Cash,” Financial Analysts Journal, 37, pp.63-71, Jul/Aug
1981.指導教授 陳稼興(Jiah-Shing Chen) 審核日期 2004-7-15 推文 plurk
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