【摘要】：資本市場(chǎng)中交易量與價(jià)格之間的關(guān)系奠定了技術(shù)分析在現(xiàn)代證券投資的核心地位,而混合分布假設(shè)則為量?jī)r(jià)關(guān)系的研究提供了理論依據(jù).相較于實(shí)務(wù)分析,量?jī)r(jià)間理論分析成果尚有可改善之處. 本文基于長(zhǎng)記憶的分形思想、多元金融時(shí)序GARCH波動(dòng)建模以及度量非線性時(shí)變相關(guān)的Copula理論,研究了量?jī)r(jià)間的深層關(guān)系,并輔之以實(shí)證與分析,做了如下兩方面的主要工作： 一方面,現(xiàn)有研究主要是把交易量作為外生變量加入到收益率波動(dòng)方程中研究量?jī)r(jià)間線性關(guān)系,本文則同時(shí)以交易量及收益率為研究主體,并考慮到波動(dòng)的持續(xù)性.運(yùn)用了二元GARCH及Copula理論的研究方法,深入、系統(tǒng)研究了量?jī)r(jià)間的相關(guān)關(guān)系. 運(yùn)用長(zhǎng)記憶金融時(shí)序檢驗(yàn)方法,針對(duì)上證指數(shù)對(duì)數(shù)收益及交易量序列進(jìn)行了長(zhǎng)記憶的檢驗(yàn).結(jié)果支持兩水平序列不存在長(zhǎng)記憶性,但波動(dòng)序列表現(xiàn)出較強(qiáng)持續(xù)性.基于長(zhǎng)記憶的波動(dòng)模型分別對(duì)交易量及收益率序列建立了長(zhǎng)記憶的波動(dòng)模型,并與短記憶模型進(jìn)行了比較.模型結(jié)果表明量?jī)r(jià)時(shí)序波動(dòng)均表現(xiàn)出長(zhǎng)記憶性,這表明短記憶模型對(duì)量?jī)r(jià)時(shí)序波動(dòng)描述不足.同時(shí)量?jī)r(jià)兩序列具有相近的分形參數(shù),這表明兩序列具有相同的持續(xù)性.利用多元GARCH模型思想,運(yùn)用二元GARCH模型分析了中國(guó)股市交易量與收益率間的關(guān)系,模型結(jié)果表明BEKK模型最優(yōu),量?jī)r(jià)間存在顯著正相關(guān). 另一方面,引入能度量非線性、尾部相關(guān)、時(shí)變相關(guān)的Copula理論分析中國(guó)股市量?jī)r(jià)關(guān)系,提出了能度量量?jī)r(jià)兩時(shí)序異方差、長(zhǎng)記憶、時(shí)變、非線性尾部相關(guān)的SJCTVPCopula-FIGARCH-t模型,通過(guò)對(duì)上證指數(shù)對(duì)數(shù)收益率及交易量?jī)尚蛄械膶?shí)證分析表明該模型優(yōu)于其他現(xiàn)有模型.結(jié)果表明上證指數(shù)在樣本期內(nèi)交易量與對(duì)數(shù)收益率本身更貼近t分布而非正態(tài)分布,均存在長(zhǎng)記憶性,且長(zhǎng)記憶性參數(shù)近似相等.量?jī)r(jià)間存在非線性上尾相關(guān)、相關(guān)性表現(xiàn)出較明顯時(shí)變的正相關(guān).
[Abstract]:The relationship between trading volume and price in capital market lays the core position of technical analysis in modern securities investment, and the mixed distribution hypothesis provides a theoretical basis for the study of quantity-price relationship. Compared with the practical analysis, the results of the theoretical analysis between quantity and price can still be improved. Based on the fractal idea of long memory, the modeling of multiple financial time series GARCH volatility and the Copula theory of measuring nonlinear time-varying correlation, this paper studies the deep relationship between quantity and price, supplemented by empirical and analysis. The following two main work has been done: on the one hand, the existing research mainly adds the trading volume as an exogenous variable to the volatility equation of return to study the linear relationship between quantity and price, while this paper takes the trading volume and rate of return as the main body of the study. And take into account the persistence of fluctuations. By using the research methods of binary GARCH and Copula theory, the correlation between quantity and price is studied systematically. The long memory financial timing test method is used to test the logarithmic return and trading volume sequence of Shanghai Stock Exchange Index. Results there was no long memory in the two level sequences, but the fluctuation sequences showed strong persistence. Based on the long memory fluctuation model, the long memory fluctuation model is established for the trading volume and the rate of return sequence, and compared with the short memory model. The results of the model show that the temporal fluctuation of quantity and price shows long memory, which indicates that the short memory model is not enough to describe the fluctuation of quantity and price. At the same time, the two sequences have similar fractal parameters, which indicates that the two sequences have the same persistence. Based on the idea of multivariate GARCH model and binary GARCH model, the relationship between trading volume and return in Chinese stock market is analyzed. The results of the model show that the BEKK model is optimal and there is a significant positive correlation between quantity and price. On the other hand, Copula theory, which can measure nonlinear, tail correlation and time-varying correlation, is introduced to analyze the quantity-price relationship in Chinese stock market, and a SJCTVPCopula-FIGARCH-t model which can measure 02:00 order heteroscedasticity, long memory, time-varying and nonlinear tail correlation is proposed. Through the empirical analysis of the logarithmic rate of return and trading volume of Shanghai Stock Exchange Index, it is shown that the model is superior to other existing models. The results show that the trading volume and logarithmic rate of return of Shanghai Stock Exchange Index are closer to t distribution than normal distribution during the sample period, and all of them have long memory, and the long memory parameters are approximately equal. There is a nonlinear upper-tail correlation between quantity and price, and the correlation shows obvious time-varying positive correlation.
【學(xué)位授予單位】：浙江工業(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)：2480617