| dc.contributor.advisor | 蔡瑞煌<br>林修葳 | zh_TW |
| dc.contributor.author (作者) | 高世儒 | zh_TW |
| dc.creator (作者) | 高世儒 | zh_TW |
| dc.date (日期) | 1998 | en_US |
| dc.date.accessioned | 18-九月-2009 19:32:06 (UTC+8) | - |
| dc.date.available | 18-九月-2009 19:32:06 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-九月-2009 19:32:06 (UTC+8) | - |
| dc.identifier (其他 識別碼) | B2002001641 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/36761 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊管理研究所 | zh_TW |
| dc.description (描述) | 86356006 | zh_TW |
| dc.description (描述) | 87 | zh_TW |
| dc.description.abstract (摘要) | 本研究首次提出以未來臨界報酬率為輸出變數，利用兩種類神經網路(Artificial Neural Network)估算國內電子股代表樣本報酬率的風險值(Value at Risk , VaR)。在研究設計上考慮到使用不同期長來計算自變項所帶來的影響而產生兩種預測方法。本研究並以回顧檢定(Backtesting )檢討藉由臨界值報酬率作為類神經估計法與一般以變異數/共變數法或蒙地卡羅模擬法所估算出VaR的差異。 綜合本研究，在學術及實務上的貢獻有下列四點： 1. 設計臨界報酬率作為估算VaR的方式，可以避免以往計算VaR時，報酬率分配主觀給定的問題。 2. 相關研究過去並未同時涉及類神經網路與VaR，而本研究首次應用類神經網路估算VaR。 3. 本文亦提出以多種不同的基本變數衡量期長來估算VaR，或可幫助界定差異的研究設計。 4. 本研究使用類神經網路可能的一項限制是報酬率臨界值 的設計方式；而類神經網路可能勝出其它預測工具的理由可能是 (1)學習到隱性因子的特性 (2)預測方式為非線性 (3)毋須依賴常態或特定分配之假設。以往類神經網路研究在賽馬決定各工具優劣時，較少探究類神經勝出或落敗的理由，而這卻是本研究設計的焦點。 | zh_TW |
| dc.description.tableofcontents | 壹、 緒論 1 貳、 文獻探討 3 一、 風險值(VAR)相關文獻 3 二、 類神經網路相關文獻 9 (一) BP神經網路 9 (二) RN與RNBP神經網路 10 參、 研究方法 12 一、 兩種預測方法 15 (一) 預測法一 15 (二) 預測法二 17 二、 VAR的驗證 19 肆、 研究結果 22 一、 三因子變異數分析的結果 22 二、 預測法一的分析結果 27 三、 預測法二的分析結果 29 四、 變異數/共變數預測法與蒙地卡羅模擬法 32 伍、 結論 34 一、 研究結論 34 (一) VaR估算方法的比較 34 (二) BP神經網路與RNBP網路系統的差異 38 (三) 兩種預測方法的差異 40 二、 研究限制與未來研究方向 42 文獻探討…………………………………………………………………………………………………..45 附錄………………………………………………………………………………………...……………...47 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#B2002001641 | en_US |
| dc.subject (關鍵詞) | 類神經網路 | zh_TW |
| dc.subject (關鍵詞) | 風險值 | zh_TW |
| dc.subject (關鍵詞) | Artificial Neural Network | en_US |
| dc.subject (關鍵詞) | Value at Risk | en_US |
| dc.title (題名) | 利用類神經網路估算國內電子股投資風險值績效 | zh_TW |
| dc.type (資料類型) | thesis | en |
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