| dc.contributor.advisor | 胡聯國 | zh_TW |
| dc.contributor.author (Authors) | 張佳誠 | zh_TW |
| dc.creator (作者) | 張佳誠 | zh_TW |
| dc.date (日期) | 2016 | en_US |
| dc.date.accessioned | 20-Jul-2016 16:38:17 (UTC+8) | - |
| dc.date.available | 20-Jul-2016 16:38:17 (UTC+8) | - |
| dc.date.issued (上傳時間) | 20-Jul-2016 16:38:17 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G1033510161 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/99289 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 國際經營與貿易學系 | zh_TW |
| dc.description (描述) | 103351016 | zh_TW |
| dc.description.abstract (摘要) | 在資產組合的優化過程中，總是希望賺取穩定的報酬以及規避不必要的風險，也因此，風險的衡量在資產組合理論中至關重要，而A. Ahmadi-Javid(2011)發表證明以相對熵為基礎的熵風險值(Entropic Value-at-Risk，簡稱EVaR)是為被廣泛使用的條件風險值(Conditional Value-at-Risk，簡稱CVaR)之上界，且EVaR在使用上更為效率，具有相當優越的性質，而本文將利用熵風險值的約當測度，去修改傳統均值–變異模型，並以臺灣股市為例，利用基因模擬退火混合演算法來驗證其在動態架構下的性質及績效，結果顯示比起傳統模型更為貼近效率前緣。 | zh_TW |
| dc.description.tableofcontents | 謝辭 i 中文摘要 ii 目錄 iii第一章 緒論 1 1.1 相對熵與熵風險值 1 1.2 投資組合理論發展歷程 2 1.3 本文創新與架構 3第二章 投資組合理論文獻概述 5 2.1 風險測度相關理論 5 2.2 效率前緣下的市場組合 10第三章 均值–熵風險值的約當模型 15 3.1 熵風險值修正係數 15 3.2 熵風險值約當測度模型 16第四章 實證研究 18 4.1 資料與數據處理 18 4.2 動態架構下的模型 18 4.3 實證結果 21 第五章 結論與建議 29 參考文獻 30 附錄 31 | zh_TW |
| dc.format.extent | 1042564 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G1033510161 | en_US |
| dc.subject (關鍵詞) | 熵風險值 | zh_TW |
| dc.subject (關鍵詞) | 動態資產組合選擇 | zh_TW |
| dc.subject (關鍵詞) | 效率前緣 | zh_TW |
| dc.subject (關鍵詞) | 市場組合 | zh_TW |
| dc.subject (關鍵詞) | EVaR | en_US |
| dc.subject (關鍵詞) | Dynamic Portfolio Selection | en_US |
| dc.subject (關鍵詞) | Efficient Frontier | en_US |
| dc.subject (關鍵詞) | Market Portfolio | en_US |
| dc.title (題名) | 熵風險值約當測度的動態資產組合理論及實證研究 | zh_TW |
| dc.title (題名) | Dynamic Portfolio Theory and Empirical Research Based on EVaR Equivalent Measure | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | [1] Ahmadi-Javid, Amir. "Entropic value-at-risk: A new coherent risk measure."Journal of Optimization Theory and Applications 155.3 (2012): 1105-1123.[2] Artzner, Philippe, et al. "Coherent measures of risk." Mathematical finance 9.3 (1999): 203-228[3] Beneplanc, Gilles, and Jean-Charles Rochet. Risk management in turbulent times. OUP USA, 2011.[4] Cochrane, John H. Asset Pricing. New York: oxford university press, 2000.[5] Dupuis, Paul, and Richard S. Ellis. A weak convergence approach to the theory of large deviations. Vol. 902. John Wiley & Sons, 2011.[6] Kafri, Oded, and Ḥaṿah Kafri. Entropy: God`s Dice Game. CreateSpace, 2013.[7] Long, Daniel Zhuoyu, and Jin Qi. "Distributionally robust discrete optimization with Entropic Value-at-Risk." Operations Research Letters 42.8 (2014): 532-538.[8] Markowitz, Harry. "Portfolio selection." The journal of finance 7.1 (1952): 77-91.[9] Philippatos, George C., and Charles J. Wilson. "Entropy, market risk, and the selection of efficient portfolios." Applied Economics 4.3 (1972): 209-220.[10] Philippatos, George C., and Charles J. Wilson. "Entropy, market risk and the selection of efficient portfolios: reply." Applied Economics 6.1 (1974): 77-81.[11] Rockafellar, R. Tyrrell, and Stanislav Uryasev. "Optimization of conditional value-at-risk." Journal of risk 2 (2000): 21-42.[12] White, D. J. "Entropy, market risk and the selection of efficient portfolios: comment." Applied Economics 6.1 (1974): 73-76. | zh_TW |
