發(fā)布時(shí)間：2018-03-01 04:01

  本文關(guān)鍵詞： 股指期貨 持倉量 交易量 波動性　出處：《復(fù)旦大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：為了研究股指期貨市場收益率、持倉量和交易量之間的關(guān)系,本文通過研究滬深300期貨合約,主力合約和月連續(xù)合約從2010年4月16日到2013年3月12日的樣本數(shù)據(jù),用前660個(gè)樣本進(jìn)行模型評價(jià),用最后43個(gè)樣本做樣本外評價(jià)。 首先本文使用GARCH模型,分別研究了持倉量和交易量對波動性的影響。研究結(jié)果發(fā)現(xiàn),對于主力合約來說,交易量的滯后項(xiàng)對收益率波動性的影響很顯著,并且交易量的滯后項(xiàng)與收益率波動性之間的關(guān)系是正向的。而持倉量的滯后項(xiàng)對于收益率波動性的影響也是非常顯著的,并且持倉量的滯后項(xiàng)與收益率波動性之間的關(guān)系是負(fù)向的。這一結(jié)論與主流的研究類似。對于非主力合約,也就是我們所選取的月連續(xù)合約樣本來說,GARCH模型回歸的結(jié)果顯示,無論是交易量,還是持倉量,與收益率波動性之間的關(guān)系都不顯著。 然后,本文使用格蘭杰因果檢驗(yàn),檢驗(yàn)三者之間的因果關(guān)系。格蘭杰因果檢驗(yàn)結(jié)果顯示,交易量和波動性之間存在雙向的格蘭杰因果關(guān)系,持倉量是波動性之間的格蘭杰原因,波動性并不是持倉量的格蘭杰原因。交易量和持倉量之間具有雙向的格蘭杰因果關(guān)系。 為了考慮這三個(gè)變量總體的相互影響,建立VAR模型,考量這三個(gè)變量整體的關(guān)系。主力合約VAR模型實(shí)證結(jié)果顯示,從模型中,我們依然可以得到交易量的滯后項(xiàng)對波動性具有正影響,持倉量的滯后項(xiàng)對收益率波動性具有負(fù)的影響,持倉量和交易量之間存在很強(qiáng)的互相影響。而滯后期的波動性對持倉量沒有影響。從月連續(xù)合約的VAR模型結(jié)果,本文發(fā)現(xiàn),對波動率主要受到自身的影響,持倉量和交易量的影響比較弱。而持倉量和交易量之間的相互影響仍舊很強(qiáng)烈,波動性的滯后項(xiàng)對交易量也有顯著的影響。 脈沖響應(yīng)分析發(fā)現(xiàn)主力合約和非主力合約的結(jié)論類似,波動率對自身有很強(qiáng)的影響,但這種影響的很短暫。交易量對收益率波動性的影響是正向的,主力合約的結(jié)果顯示影響很大,月連續(xù)合約的結(jié)果顯示影響比較小。持倉量對收益率波動性的影響時(shí)負(fù)向的,但這種影響比較微小,持續(xù)期較短。交易量和持倉量之間的影響較為強(qiáng)烈,而波動性對持倉量和交易量幾乎沒有影響。 最后,本文使用樣本外43個(gè)數(shù)據(jù)進(jìn)行預(yù)測,并進(jìn)行預(yù)測評價(jià),預(yù)測評價(jià)結(jié)果顯示,VAR模型比GARCH模型具有更好的效果。
[Abstract]:In order to study the relationship among yield, position and trading volume of stock index futures market, this paper studies the sample data of Shanghai and Shenzhen 300 futures contracts, main contracts and monthly continuous contracts from April 16th 2010 to March 12th 2013. The first 660 samples were used to evaluate the model and the last 43 samples were used to evaluate the model. First of all, we use GARCH model to study the effect of position and trading volume on volatility. The results show that for the main contracts, the lag term of trading volume has a significant impact on the return volatility. And the relationship between the lag term of trading volume and the volatility of return is positive, and the influence of the lag term of position on the volatility of return is also very significant. And the relationship between the lag term of position and the volatility of yield is negative. This conclusion is similar to the mainstream research. For the non-main contracts, that is, the samples of the monthly continuous contracts we selected, the GARCH model regression results show that, The relationship between trading volume and yield volatility is not significant. Then, the Granger causality test is used to test the causality between the three. The result of Granger causality test shows that there is a two-way Granger causality between trading volume and volatility. Position is the Granger cause of volatility, volatility is not Granger cause of position. There is a two-way Granger causality between trading volume and position. In order to consider the interaction of the three variables, the VAR model is established and the overall relationship of the three variables is considered. The empirical results of the main contract VAR model show that, We can still find that the lag term of trading volume has a positive effect on volatility, and the lag term of position has a negative effect on the volatility of yield. There is a strong interaction between position and trading volume, but the lag volatility has no effect on position. From the results of VAR model of monthly continuous contract, we find that volatility is mainly affected by itself. The influence of position size and trading volume is weak, while the interaction between position and trading volume is still very strong, and the lag term of volatility also has a significant impact on trading volume. Impulse response analysis shows that the main contract and non-main contract have similar conclusions, volatility has a strong impact on itself, but this effect is very short-lived. The effect of trading volume on yield volatility is positive. The results of the main contracts show a great impact, and the results of the monthly successive contracts show a relatively small impact. The impact of positions on yield volatility is negative, but this effect is relatively small. The influence between trading volume and position is stronger, while volatility has little effect on position and trading volume. Finally, 43 data samples are used to predict and evaluate the prediction. The prediction results show that the VAR model is more effective than the GARCH model.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F224;F724.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 錢瑞梅;王永龍;;中國燃料油期貨價(jià)格波動性研究[J];安徽師范大學(xué)學(xué)報(bào)(人文社會科學(xué)版);2008年02期

2 文玉春;;臺灣股指期貨收益波動性與交易量、持倉量考察[J];商業(yè)研究;2010年10期

3 劉卉;李曄;王遠(yuǎn)志;;中國期貨市場波動性、交易量、市場深度動態(tài)關(guān)系的日內(nèi)特征分析[J];長春理工大學(xué)學(xué)報(bào)(社會科學(xué)版);2005年04期

4 葉舟,李忠民,葉楠;期貨市場交易量與收益率及其波動關(guān)系的實(shí)證研究——ARMA—EGARCH—M模型的應(yīng)用[J];系統(tǒng)工程;2005年04期

5 徐劍剛;唐國興;;期貨波動與交易量和市場深度關(guān)系的實(shí)證研究[J];管理科學(xué)學(xué)報(bào);2006年02期

6 周仁才;;股指期貨交易量與股指現(xiàn)貨波動關(guān)系研究——來自香港恒生指數(shù)的實(shí)證[J];上海立信會計(jì)學(xué)院學(xué)報(bào);2008年04期

7 田新民,沈小剛;SHFE與LME期銅價(jià)格因果關(guān)系分析[J];首都經(jīng)濟(jì)貿(mào)易大學(xué)學(xué)報(bào);2005年03期

8 周志明,唐元虎,施麗華;中國期市收益率波動與交易量和持倉量關(guān)系的實(shí)證研究[J];上海交通大學(xué)學(xué)報(bào);2004年03期

9 徐鵬;;我國期貨鋁價(jià)格波動與成交量和持倉量動態(tài)關(guān)系的實(shí)證分析[J];世界經(jīng)濟(jì)情況;2006年13期

10 華仁海,仲偉俊;我國期貨市場期貨價(jià)格波動與成交量和空盤量動態(tài)關(guān)系的實(shí)證分析[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2004年07期

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本文編號：1550273