| 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) | https://ah.lib.nccu.edu.tw/item?item_id=112296 | - |
| 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 \n中文摘要 ii \n目錄 iii\n第一章 緒論 1\n 1.1 相對熵與熵風險值 1\n 1.2 投資組合理論發展歷程 2\n 1.3 本文創新與架構 3\n第二章 投資組合理論文獻概述 5\n 2.1 風險測度相關理論 5\n 2.2 效率前緣下的市場組合 10\n第三章 均值–熵風險值的約當模型 15\n 3.1 熵風險值修正係數 15\n 3.2 熵風險值約當測度模型 16\n第四章 實證研究 18\n 4.1 資料與數據處理 18\n 4.2 動態架構下的模型 18\n 4.3 實證結果 21\n 第五章 結論與建議 29\n 參考文獻 30\n 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.\n[2] Artzner, Philippe, et al. "Coherent measures of risk." Mathematical finance 9.3 (1999): 203-228\n[3] Beneplanc, Gilles, and Jean-Charles Rochet. Risk management in turbulent times. OUP USA, 2011.\n[4] Cochrane, John H. Asset Pricing. New York: oxford university press, 2000.\n[5] Dupuis, Paul, and Richard S. Ellis. A weak convergence approach to the theory of large deviations. Vol. 902. John Wiley & Sons, 2011.\n[6] Kafri, Oded, and Ḥaṿah Kafri. Entropy: God`s Dice Game. CreateSpace, 2013.\n[7] Long, Daniel Zhuoyu, and Jin Qi. "Distributionally robust discrete optimization with Entropic Value-at-Risk." Operations Research Letters 42.8 (2014): 532-538.\n[8] Markowitz, Harry. "Portfolio selection." The journal of finance 7.1 (1952): 77-91.\n[9] Philippatos, George C., and Charles J. Wilson. "Entropy, market risk, and the selection of efficient portfolios." Applied Economics 4.3 (1972): 209-220.\n[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.\n[11] Rockafellar, R. Tyrrell, and Stanislav Uryasev. "Optimization of conditional value-at-risk." Journal of risk 2 (2000): 21-42.\n[12] White, D. J. "Entropy, market risk and the selection of efficient portfolios: comment." Applied Economics 6.1 (1974): 73-76. | zh_TW |
