本文關(guān)鍵詞：灰色-GARCH混合模型及其在股票指數(shù)中的應(yīng)用　出處：《西北農(nóng)林科技大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

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【摘要】：改革開放以來，中國(guó)的金融證券市場(chǎng)得到了很好的和完善的發(fā)展，無論男女老少越來越多的國(guó)民參與進(jìn)來，形成了人人參與股票投資的熱潮，隨著股市的跌宕起伏或喜或悲，對(duì)于普通的股民，股票市場(chǎng)的風(fēng)云變幻一直是他們心中既怕又愛的雙刃劍，對(duì)于投資機(jī)構(gòu)和大股東而言，股票市場(chǎng)的波動(dòng)是他們規(guī)避風(fēng)險(xiǎn)的重要依據(jù)，對(duì)于市場(chǎng)監(jiān)管機(jī)構(gòu)來說，股市的波動(dòng)一直是其市場(chǎng)監(jiān)管有效性的重要度量和市場(chǎng)政策制定的重要依據(jù)。由此可見，波動(dòng)率的建模和描述一直都是各方關(guān)注的焦點(diǎn)和重點(diǎn)，對(duì)學(xué)者和股市從業(yè)者都有極其重要的意義,并且波動(dòng)率的計(jì)算也為VaR數(shù)學(xué)模型的建立和計(jì)算提供了依據(jù)和基礎(chǔ)。 因此，為了描述和刻畫市場(chǎng)的波動(dòng)，本文在時(shí)間序列模型其中主要是廣義自回歸條件異方差模型和灰色模型基礎(chǔ)上，認(rèn)真研究和總結(jié)了以前學(xué)者和專家的研究成果，提出了新陳代謝灰色-廣義自回歸條件異方差混合模型，即灰色-GARCH混合模型。以前的研究結(jié)果表明，廣義自回歸條件異方差模型的殘差項(xiàng)應(yīng)會(huì)隨著時(shí)間的變動(dòng)而受到過去價(jià)格波動(dòng)或信息沖擊等灰色不確定性因素的影響，并隨之變化，這對(duì)于廣義自回歸條件異方差模型來說是一個(gè)很難明確描述和表達(dá)的變量，其結(jié)果就是直接的影響了廣義自回歸條件異方差模型對(duì)于波動(dòng)率的刻畫和估計(jì)。因此本文采用灰色系統(tǒng)理論的以少量數(shù)據(jù)資料即能建立起不錯(cuò)的預(yù)測(cè)模型和對(duì)灰色不確定性因素的良好描述和預(yù)測(cè)等良好特性，對(duì)廣義自回歸條件異方差模型內(nèi)的殘差項(xiàng)建立灰色模型，用這兩個(gè)模型得到灰色-GARCH混合模型，用它來重新描述和估計(jì)市場(chǎng)的波動(dòng)。因?yàn)榇四Ｐ蛻?yīng)用到灰色模型和廣義自回歸條件異方差模型，是這兩個(gè)模型的有機(jī)的結(jié)合，具有灰色模型對(duì)灰色信息的良好撲捉和廣義自回歸條件異方差模型對(duì)波動(dòng)率的很好的表達(dá)，所以叫做灰色-GARCH混合模型。 為了建立灰色-GARCH混合模型，本文首先介紹了時(shí)間序列模型和灰色模型的發(fā)展和其現(xiàn)在的研究現(xiàn)狀，并且對(duì)這兩類模型的建模步驟和方法進(jìn)行了比較全面的介紹，在其基礎(chǔ)上本文建立了灰色-GARCH混合模型，隨后采用道瓊斯中國(guó)網(wǎng)站數(shù)據(jù)，運(yùn)用Eviews軟件和Matlab軟件，，對(duì)選取的道瓊斯中國(guó)88指數(shù)的數(shù)據(jù)進(jìn)行了實(shí)證分析，結(jié)果表明，與廣義自回歸條件異方差模型相比較，本文所建立的灰色-GARCH混合模型對(duì)波動(dòng)的表達(dá)更貼近市場(chǎng)實(shí)際，其對(duì)波動(dòng)率的描述和刻畫也更加準(zhǔn)確，并且對(duì)于在此基礎(chǔ)上建立的VaR數(shù)學(xué)模型的準(zhǔn)確性提供了可靠的依據(jù)和數(shù)據(jù)保證。
[Abstract]:Since the reform and opening up, China's financial and securities market has been a very good and perfect development, regardless of the men, women and children more and more citizens participate in the formation of everyone's participation in stock investment upsurge. With the ups and downs of the stock market or happy or sad, for the ordinary shareholders, the stock market has been changing in their hearts both afraid and love double-edged sword, for investment institutions and major shareholders. The volatility of the stock market is an important basis for them to avoid risks. For the market regulators, the volatility of the stock market has always been an important measure of the effectiveness of their market regulation and an important basis for the formulation of market policies. The modeling and description of volatility has always been the focus and focus of all parties concerned, which is of great significance to scholars and stock market practitioners. The calculation of volatility also provides the basis for the establishment and calculation of VaR mathematical model. Therefore, in order to describe and characterize the volatility of the market, this paper based on the time series model, mainly generalized autoregressive conditional heteroscedasticity model and grey model. This paper studies and summarizes the research results of previous scholars and experts, and puts forward the mixed model of metabolism gray and generalized autoregressive conditional heteroscedasticity, that is, the grey GARCH mixed model. The residual term of generalized autoregressive conditional heteroscedasticity model should be affected by grey uncertainty such as price fluctuation or information shock with time change. For the generalized autoregressive conditional heteroscedasticity model, it is difficult to describe and express the variables clearly. The result is that it directly affects the characterization and estimation of volatility in generalized autoregressive conditional heteroscedasticity model. Therefore, the grey system theory can be used to establish a good prediction model and grey model with a small amount of data. Good description and prediction of color uncertainty. The grey model is established for the residual terms in the generalized autoregressive conditional heteroscedasticity model and the grey GARCH mixed model is obtained by using these two models. It is used to redescribe and estimate the volatility of the market because the application of this model to the grey model and the generalized autoregressive conditional heteroscedasticity model is an organic combination of the two models. The grey GARCH mixed model is called the grey GARCH mixed model because of the good capture of grey information by grey model and the good representation of volatility by generalized autoregressive conditional heteroscedasticity model. In order to establish the grey GARCH mixed model, this paper firstly introduces the development of the time series model and the grey model and its present research status. And the modeling steps and methods of these two models are introduced comprehensively. On the basis of these models, the grey GARCH mixed model is established, and then the Dow Jones website data is used. Using Eviews software and Matlab software, the data of Dow Jones China 88 index are analyzed. The results show that the data are compared with the generalized autoregressive conditional heteroscedasticity model. The grey GARCH hybrid model presented in this paper is more close to the market reality and its description and characterization of volatility is more accurate. It also provides a reliable basis and data guarantee for the accuracy of the VaR mathematical model established on this basis.
【學(xué)位授予單位】：西北農(nóng)林科技大學(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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