本文關鍵詞： BARRA模型 ETF 歸因分析 風險預測　出處：《首都經(jīng)濟貿(mào)易大學》2014年碩士論文　論文類型：學位論文

【摘要】：ETF (Exchange Trade Fund),中文名為交易所交易基金,1993年自美國誕生,隨后逐步在全球金融市場蔓延開來,經(jīng)過20多年的發(fā)展,截止到2013年年底,全球的ETF規(guī)模已經(jīng)達到了2.387萬億美元,占比達到了全球基金市場(剔除貨幣和FOF)規(guī)模的10%,ETF已成為基金市場不可或缺的組成部分。華夏上證50ETF-國內首只ETF,自2004年開始在上海證券交易所上市交易,經(jīng)過9年多的發(fā)展,截至2013年底,上交所共發(fā)行了48只ETF,市值達到1100億,交易額超過6200億,覆蓋范圍包括股票、債券、商品、貨幣等。 本文根據(jù)BARRA模型的研究理念以及聯(lián)系國內ETF市場的現(xiàn)狀,并且在基于對A股市場數(shù)據(jù)的長期分析和風險特性的數(shù)量化篩選的條件下,共選取了31個風格因子和26個行業(yè)因子共57公共因子,涵蓋了從各個維度衡量的個股風險和收益的相關情況。通過聚類分析和主成份分析并進行降維處理以后最終選出12個行業(yè)因子和10個風格因子來對于國內ETF市場的收益情況進行歸因研究以及對其風險波動情況進行解釋。 文章選取上證50ETF和創(chuàng)業(yè)ETF作為研究實例來對ETF的市場業(yè)績進行分析,并且利用銀河99基金來研究ETF的波動性。首先求出22個公共因子對于個股的因子報酬,然后利用50ETF的超額收益和22個公共因子的因子報酬進行時間序列回歸,得出共有18個公共因子對50ETF有影響。同理對創(chuàng)業(yè)ETF進行分析,由于創(chuàng)業(yè)ETF成立時間短,在進行SPSS分析時采用Backward法,共有16個公共因子進入模型,通過檢驗最終得出只有8個公共因子對創(chuàng)業(yè)ETF的收益會產(chǎn)生影響。另外,通過對公共因子的波動性進行研究,發(fā)現(xiàn)公共因子的風險波動率走勢與銀河99基金市場的風險波動率走勢基本一致。
[Abstract]:ETF exchange Trade fund, named Exchange Trade Fund in Chinese, was born from the United States in 1993, and then gradually spread in the global financial market. After more than 20 years of development, by the end of 2013, the global ETF scale had reached two tillion three hundred and eighty-seven billion US dollars. The 10 ETFs, which have reached the size of the global fund market (excluding currencies and FOFs), have become an integral part of the fund market. China Shanghai 50ETF- the first ETF in China, has been listed and traded on the Shanghai Stock Exchange since 2004, after more than nine years of development. By end of 2013, 48 ETFs had been issued on the Shanghai Stock Exchange, with market value reaching 110 billion and trading more than 620 billion, covering stocks, bonds, commodities and currencies. According to the research idea of BARRA model and the current situation of domestic ETF market, and based on the long-term analysis of A-share market data and quantitative screening of risk characteristics, A total of 31 style factors and 26 industry factors were selected and 57 common factors were selected. Through cluster analysis and principal component analysis and dimensionality reduction, 12 industry factors and 10 style factors are selected for the domestic ETF market. The return of the attribution study and its risk volatility to explain. This paper selects Shanghai 50 ETF and entrepreneurial ETF as research examples to analyze the market performance of ETF, and uses Yinhe 99 Fund to study the volatility of ETF. Then using the excess returns of 50 ETFs and the factor returns of 22 public factors for time series regression, it is concluded that 18 public factors have influence on 50 ETFs. In the analysis of SPSS, the Backward method is used. There are 16 public factors entering the model. Finally, only 8 public factors have an impact on the income of entrepreneurial ETF. In addition, the volatility of public factors is studied. It is found that the trend of risk volatility of common factors is basically consistent with that of Yinhe 99 fund market.
【學位授予單位】：首都經(jīng)濟貿(mào)易大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F832.51;F224

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