請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35118完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道(Yuh-Dauh Lyuu) | |
| dc.contributor.author | Wei-Cheng Hsu | en |
| dc.contributor.author | 許維城 | zh_TW |
| dc.date.accessioned | 2021-06-13T06:41:25Z | - |
| dc.date.available | 2020-06-07 | |
| dc.date.copyright | 2011-08-11 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-07-25 | |
| dc.identifier.citation | [1] Lin, C. J. and Weng, R. C. (2004), “Simple Probabilistic Predictions for Support Vector
Regression.” Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. [2] Lyuu, Y.D. (2002), Financial Engineering and Computation. Cambridge, UK: Cambridge University Press. [3] Chang, C. C. and Lin, C. J. (2001), “LIBSVM: a Library for Support Vector Machines.” Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. [4] Steve, R. G. (1998), “Support Vector Machines for Classification and Regression.” Technical Report, Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science, University of Southampton. [5] Gao, J. B., Gunn, S. R., Harris, C. J., and Brown, M. (2002), “A Probabilistic Framework for SVM Regression and Error Bar Estimation.” Machine Learning, Vol. 46, 71–89. [6] Hull, C. J. (2000), Options, Futures and Other Derivatives. 4e, Englewood Cliffs, NJ: Prentice-Hall. [7] Chen, Y., Peng, L., and Abraham, A. (2006), “Stock Index Modeling Using Hierarchical Radial Basis Function Networks.” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4253, LNAI - III, 398–405. [8] Smola , A. J., and Scholkopf, B. S. (2004), “A Tutorial on Support Vector Regression.” Statistics and Computing, Vol. 14, No. 3, 199–222. [9] Huang, S. C., Chuang, P. J., Wu, C. F., and Lai, H. J. (2010), “Chaos-Based Support Vector Regressions for Exchange Rate Forecasting.” Expert Systems with Applications, 8590–8598. [10] Hsu, C. W., Chang C. C., and Lin, C. J. (2010), “A Practical Guide to Support Vector Classification.” Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. [11] Bach, F. R., Lanckriet, R. G., and Jordan, M. I. (2004), “Multiple Kernel Learning, Conic Duality, and the SMO Algorithm.” In Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada, 6. [12] Kim, K. J. (2003), “Financial Time Series Forecasting Using Support Vector Machines.” Neurocomputing, Vol. 55, 307–319. [13] Tsai, C. F. and Wang, S. P. (2009), “Stock Price Forecasting by Hybrid Machine Learning Techniques.” In Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1, March 18–20, Hong Kong, China, 755–760 [14] Pai, P. F., and Lin, C. S. (2005), “A Hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting.” Omega, Vol. 33, 497–505. [15] Cao, L., and Tay, F. E. H. (2001), “Financial Forecasting Using Support Vector Machines.” Neural Computing & Applications, Vol. 10, No. 2, 184–192. [16] Chen, S., Jeong, K., and Hardle, W. (2008), “Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns.” SFB 649, Discussion Paper of Economic Risk Conference, Berlin. [17] Qin, H., Dou, D., and Fang, Yue. (2010), “Financial Forecasting with Gompertz Multiple Kernel Learning.” In Proceedings of 2010 IEEE 10th International Conference on Data Mining (ICDM), Sydney, Australia, 983–988. [18] Lin, C. J. (2006), “A Guide to Support Vector Machines.” Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. [1] 黃漢堂，整合支撐向量機模型(SVM)與市場基礎模型應用於台灣營建公司財務危機預測之 研究，國立台灣大學土木工程學研究所，碩士論文，民國100 年。 [2] 倪衍森、吳曼華、鄭亦妏，在Black-Scholes 評價模型下台指選擇權最適波動性估計方 法之研究，管理科學研究， Vol.2, No.1,，民國96 年。 [3] 黃祺偉，多核心支援向量迴歸機應用於股價預測，國立中山大學 電機工程學系，碩士論 文，民國98 年。 [4] 台灣期貨交易所網站：http://www.taifex.com.tw/ | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35118 | - |
| dc.description.abstract | 選擇權是一種衍生性金融商品，在市場上的選擇權可由Black-Scholes方程式推導出隱含波動度，意味著投資人對於未來標的物實際波動度的預期，然而隱含波動度與未來標的物實際波動度，往往並不是很接近。
本論文應用支援向量迴歸機預測臺灣加權指數波動度。訓練模型期間自2006年12月21日到2011年2月16日符合預設條件之成交資訊。預測模型期間自2007年12月20日到2011年3月16日符合預設條件之成交資訊，每一筆預測模型中的資料均產生一組預測波動度與隱含波動度做比較，以期更為貼近標的物實際波動度。 從實驗的結果可得知，以均方根誤差而言，在訓練期間為一單位的情形下，有優化參數的預測波動度與無優化參數的預測波動度相較於實際波動度的均方根誤差較隱含波動度低，在訓練期間為三單位、六單位、十二單位下，有優化參數的預測波動度與無優化參數的預測波動度相較於實際波動度的均方根誤差較隱含波動度高。以平均誤差而言，隨著訓練期間在一單位、三單位、六單位、十二單位下，無優化參數的預測波動度高估實際波動度。有優化參數的預測波動度，在訓練期間為一單位、六單位下，高估實際波動度，在訓練期間為三單位、十二單位下，低估實際波動度。隱含波動度均為低估實際波動度。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-13T06:41:25Z (GMT). No. of bitstreams: 1 ntu-100-R98944035-1.pdf: 3273037 bytes, checksum: e58a3f2a9080aed053442bd2340738eb (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 誌謝......................................................................................................................................................I
摘要.................................................................................................................................................... II ABSTRACT.....................................................................................................................................III 目錄...................................................................................................................................................IV 第一章導論...............................................................................................................................1 1.1 研究動機.................................................................................................................................. 1 1.2 論文架構.................................................................................................................................. 3 第二章 背景知識........................................................................................................................5 2.1 選擇權簡介............................................................................................................................... 5 2.2 支援向量迴歸機....................................................................................................................... 7 第三章 研究方法......................................................................................................................13 3.1 實驗資料來源與模型資料切割方法........................................................................................ 13 3.2 模型資料解釋及名詞定義....................................................................................................... 15 3.3 實驗流程................................................................................................................................. 17 3.4 系統實作................................................................................................................................. 21 第四章 實驗結果......................................................................................................................22 4.1 實驗(一) ................................................................................................................................. 27 4.2 實驗(二) ................................................................................................................................. 29 4.3 實驗(三) ................................................................................................................................. 31 4.4 實驗(四) ................................................................................................................................. 33 4.5 實驗結論................................................................................................................................. 35 第五章 結論與未來研究方向..................................................................................................39 參考英文文獻...................................................................................................................................40 參考中文文獻...................................................................................................................................42 | |
| dc.language.iso | zh-TW | |
| dc.subject | 實際波動度 | zh_TW |
| dc.subject | 選擇權 | zh_TW |
| dc.subject | 隱含波動度 | zh_TW |
| dc.subject | 支援向量迴歸 | zh_TW |
| dc.subject | 預測波動度 | zh_TW |
| dc.subject | realized volatility | en |
| dc.subject | forecasting volatility | en |
| dc.subject | option | en |
| dc.subject | implied volatility | en |
| dc.subject | support vector regression | en |
| dc.title | 以支援向量迴歸機器學習方法預測實際波動度 | zh_TW |
| dc.title | Forecasting Realized Volatility with Support Vector Regression | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 戴天時(Tian-Shyr Dai),金國興(Guo-Sing Jin),張經略(Ching-Lueh Chang) | |
| dc.subject.keyword | 選擇權,隱含波動度,支援向量迴歸,預測波動度,實際波動度, | zh_TW |
| dc.subject.keyword | option,implied volatility,support vector regression,realized volatility,forecasting volatility, | en |
| dc.relation.page | 42 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-07-25 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-100-1.pdf 未授權公開取用 | 3.2 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。