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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105474


    Title: 高頻資料關聯結構分析在最適資產配置投資組合上之應用與比較 : 以臺灣證券市場為例
    Other Titles: Comparisons of high frequency data by copula methods in optimal assets allocation : the empirical analysis in Taiwan equity markets
    Authors: 吳致霖;Wu, Chih-Lin
    Contributors: 淡江大學統計學系碩士班
    林志娟;Lin, Jyh-Jiuan
    Keywords: 高頻資料;日內資料;關聯結構模型;平均數-變異數投資組合模型;High-Frequency Data;Intraday data;Copula Models;Mean-variance portfolio model
    Date: 2015
    Issue Date: 2016-01-22 14:56:51 (UTC+8)
    Abstract: 在瞬息萬變的金融市場中,要如何透過分析過去歷史資料而獲得未來投資方向之訊息,一直是投資者所關注的議題,而高頻金融資料也比一般金融資料隱含有更多訊息,因此要如何從高頻金融資料中找出有用的訊息,並且透過不同的分析方法與比較,從中篩選出最適合投資人之投資策略,也是本文致力研究之方向。

    本研究主要採用高頻金融資料,以及搭配關聯結構模型方法來估計證券報酬間的相關性,並進行資產配置最佳化。透過觀察累積報酬率的變化,找出在均異效率投資組合下,不同關聯結構模型與不同證券報酬的邊際分配,會如何對投資績效產生影響,並歸納出其特性。

    另外在不同公司持股數下,從六種證券報酬率邊際分配(常態分配、T分配、雙指數分配、一般化柏拉圖分配、一般化誤差分配、一般化極值分配)中選出幾種配適較優之分配,以及兩種關聯結構模型(常態關聯結構、T關聯結構),搭配產生的投資組合之投資績效來進行分析。

    實證分析的結果顯示,將高頻資料使用以周為調整資產配置時間、持股公司數為25、使用一般化柏拉圖分配做為證券報酬之邊際分配,並且使用常態關聯結構模型,代入最佳化投資組合進行資產配置,所得到的投資組合最能夠快速的反映市場狀況,並且有效的降低投資風險,本研究建議積極型的投資人使用此投資策略來提升其投資績效。
    How to get the information and analyze is one of the most important issue in financial market. This research devotes to providing the investors the proper investment strategies through the optimal asset allocation. There are several aspects need to be considered before one could approach the optimal strategies.

    First of all, the inputs of the objective function are the key ingredients to reach the minimum risk. Secondly, the marginal densities fitness of the assets and the correlation between the assets could fine tune the input estimation if the statistical methods are used properly. At last, the data frequency is also another ingredient of information. Different data frequency provides different microstructure information, therefore, lead to a different strategy.

    To achieve the goal, this research adopts the best fitted asset return marginal distribution out of six marginal densities, Generalized Pareto distribution. Two copula models (normal copula and T copula) are incorporated to catch the correlation between the assets. Different portfolio sizes and rolling agenda settings are investigated.

    Using intra-day 5 minutes high frequency data, it is found empirically that the optimal portfolio return can be reached at portfolio size 25, adopting Generalized Pareto distribution and normal copula model, rolling out and reinvesting weekly.
    Appears in Collections:[統計學系暨研究所] 學位論文

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