本文關(guān)鍵詞：基于回歸模型的指數(shù)追蹤問題及實(shí)證分析　出處：《云南財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 指數(shù)追蹤 線性回歸 分位數(shù)回歸 上證50指數(shù)

【摘要】：指數(shù)追蹤是一種用少量的成分股來追蹤某一市場(chǎng)指數(shù)走勢(shì)的方法,它是消極投資組合管理策略中的一種,近年來在我國(guó)發(fā)展迅速。現(xiàn)在許多基金公司和投資者用指數(shù)追蹤進(jìn)行投資,因此,如何能夠有效追蹤市場(chǎng)指數(shù),受到了越來越多的人關(guān)注,其中就有很多對(duì)它有興趣的學(xué)者。學(xué)者希望從市場(chǎng)指數(shù)以及其成分股的歷史數(shù)據(jù)中尋找到一種合適的方法,使得追蹤效果更好,從而為基金公司和投資者提供更多的選擇。本文建立了指數(shù)追蹤的線性回歸模型和分位數(shù)回歸模型,然后通過三種選股法得到了三個(gè)樣本股空間,最后在這三個(gè)樣本股空間上對(duì)模型進(jìn)行實(shí)證分析。由于線性回歸模型有很多條件假設(shè),且參數(shù)容易受到極端值的影響,為了避免這些,我們提出了指數(shù)追蹤的分位數(shù)回歸模型。具體來說,本文選取了2016年上半年的上證50指數(shù)為目標(biāo)指數(shù),構(gòu)建兩個(gè)指數(shù)追蹤模型并給出約束條件,分別在三個(gè)樣本股空間上選股,最后通過對(duì)真實(shí)數(shù)據(jù)的擬合來得到追蹤效果較好的追蹤組合,預(yù)測(cè)未來30天的收益,并與上證50的收益進(jìn)行對(duì)比。本文得到的主要結(jié)論如下:(1)在三種選股方法中,最大相關(guān)系數(shù)選股法的整體追蹤效果最好,且值一般是越小越好;(2)對(duì)比兩個(gè)模型,在追蹤效果方面,線性回歸模型明顯優(yōu)于分位數(shù)模型,但是分位數(shù)回歸模型可以獲得更大的超額收益;(3)在不同的選股法下值的改變對(duì)模型的影響不同。
[Abstract]:Index tracking is a method of tracking a market index with a small number of component stocks. It is one of the negative portfolio management strategies. In recent years, there has been a rapid development in China. Now many fund companies and investors use index tracking to invest. Therefore, how to track the market index effectively has attracted more and more attention. Among them, there are many scholars who are interested in it. Scholars hope to find a suitable way from the market index and the historical data of its constituent shares to make the tracking effect better. In this paper, the linear regression model and quantile regression model of exponential tracking are established, and then three sample stock spaces are obtained by three stock selection methods. Finally, the empirical analysis of the model is carried out on the three sample stock spaces. Because the linear regression model has a lot of assumptions, and the parameters are easily affected by extreme values, in order to avoid these. In this paper, we propose a quantile regression model for exponential tracking. Specifically, we select the Shanghai Stock Exchange 50 index in the first half of 2016 as the target index, construct two index tracking models and give the constraint conditions. Stock selection in the three sample stock space, finally through the fitting of the real data to get a better tracking effect of tracking combination, forecast the next 30 days of income. The main conclusions of this paper are as follows: 1) among the three stock selection methods, the overall tracking effect of the maximum correlation coefficient stock selection method is the best, and the value is generally the smaller the better; 2) comparing the two models, the linear regression model is better than the quantile model in tracking effect, but the quantile regression model can get more excess income. 3) under different stock selection methods, the effect of value change on the model is different.
【學(xué)位授予單位】：云南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51

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