臺灣博碩士論文加值系統

摘要

自衍生性金融商品問世以來，各種投資工具及投資標的不斷地推陳出新，但金融市場中仍以Markowitz之投資組合概念進行投資行為。投資標的及相關衍生性金融商品的突破創新，雖然為金融市場提供更多的分散資金之管道，卻也使投資者面對更高之風險，進而促使投資者必須針對該風險值加以評估，並採行相關的規避措施。換言之，對投資者與企業經營者而言，投資組合及其風險值為投資行為上不可或缺的觀念。有鑑於此，本研究將風險值的概念與投資組合理論予以合併，探討投資者如何決定最適投資組合下進行風險值分析，並針對風險值加以控管。

本研究之實證資料涵蓋匯率、股票、基金及黃金市場中10項資產之日報酬資料，透過MV模型及個別投資者之風險趨避程度，決定最適完整性投資組合之標的與其標的所對應之最適權重，並進一步針對該最適投資組合運用歷史模擬法、以GARCH為先驗模型之蒙地卡羅模擬法與EGARCH模型進行風險值分析，最後透過RMSE、MAE及回溯測試，評估各種風險值模型預測能力之績效。

實證結果顯示，當樣本期間包含全球金融風暴期間時，在市場衰退時投資者應將資金比重投資於具有保值與避險效果之黃金商品，且構成完整性投資組合後，可有效地降低投資風險。此外，各種風險值模型的樣本外預測結果顯示，以GARCH模型為樣本內先驗模型之蒙地卡羅模擬法估計之風險值預測效果為最佳；而使用歷史模擬法與EGARCH模型所估算之風險值則有高估之現象。

Abstract

As the financial derivatives been rapidly developed, various kinds of investment tools have been constantly renovateing. In fact, Markowitz’s portfolio concept is still the benchmark for most investment behavior in the financial market. Although the innovation of investment tools and related financial derivatives can offer more scattered fund for financial market, more risk derived from the fluctuation of asset price comes with that. Therefore, the concept of risk management becomes more important for investors and managers. Based on this reason, this study combines the concept of VaR with the theory of portfolio to investigate how should investors analyze and manage the VaR under the chosen optimum portfolio.

The 10 component assets in portfolio contain foreign exchange rates, stocks, mutual funds and gold. By using Mean-Variance approach and individual investor’s risk aversion altitude, we can first decide optimal investment portfolio, including component assets and their weights. Furthermore, employing historical simulation, Mote Carlo simulation combined with GARCH model, and EGARCH model we can evaluate the VaR of that optimal portfolio. Finally, through the RMSE, MAE and back test we can evaluate each model’s forecasting performance.

Empirical study shows that during the period of Subprime Mortgage storm (the stage of economic recession), investors should invest in gold market to get better hedge and preserve asset value, and the decided optimal portfolio can actually reduce investment risk. Moreover, from the results of the out-of-sample forecasting we know that the metempirical model to GARCH of Monte Carlo Simulation is the best one to forecast the VaR, and the Historical Simulation and EGARCH model have over-evaluated the VaR.

目錄

中文摘要 Ⅰ
Abstract Ⅱ
誌謝辭 Ⅲ
目錄 Ⅳ
表目錄 VI
圖目錄 VII
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 6
第三節 研究流程與架構 7
第貳章 文獻回顧 9
第一節 投資組合分析 9
第二節 風險值模型 13
第三節 預測模型績效評估 18
第參章 研究方法與實證模型 19
第一節 投資組合模型 19
第二節 VaR之定義與估計模型 26
第三節 預測能力之衡量與回溯測試 35
第肆章 實證結果與分析 37
第一節 研究範圍及資料來源 37
第二節 投資權重分析 40
第三節 完整性投資組合風險值評估 45
第四節 風險模型之比較與回溯測試 54

第伍章 結論與建議 57
第一節 結論 57
第二節 未來延伸與建議 59
參考文獻 60
附錄 65


表目錄

表1-1-1 VaR評估方法之比較 4
表4-1-1 投資標的日報酬之基本統計分析 39
表4-2-1 效率前緣上可行性投資組合 41
表4-2-2 不同風險偏好下最適完整性投資組合 43
表4-3-1 最適風險性資產之相關係數 48
表4-3-2 歷史模擬法最適完整性投資組合風險值估計結果 48
表4-3-3 GARCH模型之係數估計結果 49
表4-3-4 蒙地卡羅模擬法最適完整性投資組合風險值估計結果 50
表4-3-5 EGARCH模型之係數估計結果 52
表4-3-6 EGARCH模型最適完整性投資組合風險值估計結果 52
表4-4-1 A=2、5時，各模型樣本外預測能力之衡量 54
表4-4-2 A=7時，各模型樣本外預測能力之衡量 55
表4-4-3 A=10時，各模型樣本外預測能力之衡量 56


圖目錄

圖1-3-1 研究流程圖 8
圖3-1-1 效率前緣與可行性投資組合之集合 20
圖3-1-2 資本市場線與最適完整性投資組合 25
圖4-2-1 不同風險趨避係數下資金投資比重之比較 44
圖4-3-1 最適權重資產與投資組合之日報酬率 46

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