本文選題：波動(dòng)性 + GARCH模型��； 參考：《長(zhǎng)春工業(yè)大學(xué)》2012年碩士論文

【摘要】：長(zhǎng)期以來(lái),股票市場(chǎng)收益率波動(dòng)性的研究歷來(lái)是金融時(shí)間序列研究的關(guān)鍵問(wèn)題,同時(shí)也是每個(gè)國(guó)家監(jiān)管機(jī)構(gòu)最關(guān)注的權(quán)衡目標(biāo),因?yàn)楣善辈▌?dòng)性是反映股票價(jià)格變化最便捷和最管用的指標(biāo)之一,同時(shí),與企業(yè)投資,財(cái)務(wù)策劃以及消費(fèi)者的消費(fèi)行為最為密切相關(guān)的。近些年來(lái),我國(guó)的股票市場(chǎng)發(fā)展迅猛,對(duì)股票波動(dòng)率的變化的度量方法需求也日益強(qiáng)烈。 GARCH模型是為金融數(shù)據(jù)而專門量體定做的條件異方差模型,其非常適合于股市收益波動(dòng)性的分析。GARCH模型(廣義自回歸條件異方差模型)是ARCH模型(自回歸條件異方差模型)的推廣形式,其充分地說(shuō)明了資產(chǎn)收益率波動(dòng)過(guò)程。 金融時(shí)間序列的特點(diǎn)為波動(dòng)集聚性、非對(duì)稱性以及尖峰厚尾性。一般地波動(dòng)集聚性由ARCH模型及其一般形式GARCH模型來(lái)刻畫,金融時(shí)間序列另一個(gè)典型的特征是非對(duì)稱性,GARCH模型中一般都假定殘差項(xiàng)是服從正態(tài)分布的,不能夠表現(xiàn)出這一特點(diǎn)。首先,文章建立了金融時(shí)間序列對(duì)于波動(dòng)性非對(duì)稱性的TAR—GARCH模型(閾自回歸GARCH模型),檢驗(yàn)股市收益波動(dòng)對(duì)于其自相關(guān)性的差異性,以及對(duì)于正負(fù)信息的差異性。其次,本文引入了虛變量,采用股市收益的虛變量GARCH模型來(lái)刻畫金融時(shí)間序列的非對(duì)稱性,通過(guò)對(duì)GARCH模型的改進(jìn),更好的反應(yīng)了金融時(shí)間序列的特點(diǎn)及股票波動(dòng)性的變化,得到了更好的擬合效果。本文將虛變量GARCH模型應(yīng)用于上證綜合指數(shù)的波動(dòng)性研究,拓展了GARCH模型在股票市場(chǎng)上的應(yīng)用。經(jīng)研究表明,GARCH模型的改進(jìn)具有非常重要的理論意義和現(xiàn)實(shí)意義,不但可以幫助投資者針對(duì)具體情況作出具體分析,而且對(duì)于政策的制定者也具有很大的參考價(jià)值。 最后,又將虛變量GARCH模型和GARCH模型的變體(即非對(duì)稱GARCH模型)在實(shí)例分析中進(jìn)行對(duì)比,更加說(shuō)明本論文創(chuàng)新點(diǎn)的適用性,即非常適合股票波動(dòng)性的分析,可以起到非常重要的作用,其意義和對(duì)數(shù)值本身的研究相比更加顯著。
[Abstract]:For a long time, the study of stock market yield volatility has always been a key issue in the study of financial time series, and it is also the most concerned tradeoff goal of every country's regulators.Because stock volatility is one of the most convenient and useful indicators to reflect changes in stock prices, and is most closely related to corporate investment, financial planning and consumer consumer behavior.In recent years, the stock market of our country has developed rapidly, and the demand for measuring the change of stock volatility has become more and more intense.The GARCH model is a conditional heteroscedasticity model tailored for financial data.GARCH model (generalized autoregressive conditional heteroscedasticity model) is a generalization of ARCH model (autoregressive conditional heteroscedasticity model), which fully illustrates the volatility process of asset return.The financial time series is characterized by volatility agglomeration, asymmetry and peak and thick tail.General volatility agglomeration is characterized by ARCH model and its general form GARCH model. Another typical feature of financial time series is asymmetric GARCH model, which is generally assumed that the residual term is obedient to normal distribution, which cannot be shown.Firstly, the paper establishes the TAR-GARCH model of financial time series for asymmetric volatility (threshold autoregressive GARCH model) to test the difference between stock market return volatility and its autocorrelation, as well as the difference of positive and negative information.Secondly, this paper introduces virtual variables, adopts the GARCH model of stock market returns to describe the asymmetry of financial time series. By improving the GARCH model, it better reflects the characteristics of financial time series and the change of stock volatility.A better fitting effect is obtained.In this paper, the virtual variable GARCH model is applied to the volatility study of Shanghai Composite Index, which extends the application of GARCH model in the stock market.The research shows that the improvement of GARCH model is of great theoretical and practical significance. It can not only help investors to make specific analysis according to the specific situation, but also have great reference value for policy makers.Finally, by comparing the variation of virtual variable GARCH model with GARCH model (i.e. asymmetric GARCH model) in the case analysis, this paper demonstrates the applicability of the innovation point of this paper, that is, it is very suitable for the analysis of stock volatility.It can play a very important role, and its significance is more significant than the study of the numerical value itself.
【學(xué)位授予單位】：長(zhǎng)春工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51

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本文編號(hào)：1759953