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  本文關(guān)鍵詞：基于ACD-UHF-GARCH模型的中國(guó)股票市場(chǎng)波動(dòng)性研究　出處：《東北大學(xué)》2013年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 股票市場(chǎng) 高頻數(shù)據(jù) 波動(dòng)性 ACD-UHF-GARCH模型

【摘要】：在市場(chǎng)經(jīng)濟(jì)的條件下,中國(guó)股票市場(chǎng)越發(fā)成熟。目前,股票市場(chǎng)的波動(dòng)性研究一直是金融研究的熱點(diǎn)。目前,很多學(xué)者對(duì)股市量?jī)r(jià)關(guān)系進(jìn)行了研究,但是很少引入時(shí)間因素,因此本文將交易量和時(shí)間因素同時(shí)納入研究的模型中,解釋持續(xù)期和交易量在價(jià)格波動(dòng)中所起的作用。應(yīng)用高頻數(shù)據(jù)研究股市波動(dòng)性實(shí)質(zhì)上是應(yīng)用交易時(shí)間間隔來(lái)測(cè)定波動(dòng)性。高頻時(shí)間序列包含市場(chǎng)微觀結(jié)構(gòu)的信息及重要的長(zhǎng)期日間現(xiàn)象的信息。本文在以往研究的基礎(chǔ)上,采集滬深兩市逐筆交易和等間隔交易的數(shù)據(jù),選用研究高頻數(shù)據(jù)的ACD模型和在應(yīng)用低頻數(shù)據(jù)測(cè)度股市波動(dòng)性表現(xiàn)較好的GARCH模型,應(yīng)用ACD模型計(jì)算出交易持續(xù)期均值,再應(yīng)用所得參數(shù)構(gòu)建處理超高頻數(shù)據(jù)的GARCH模型,即UHF-GARCH模型。在建立UHF-GARCH模型時(shí),本文不僅考慮到交易的持續(xù)期,同時(shí)還考慮到交易量對(duì)股市收益率和波動(dòng)性的影響。本文分別選取了個(gè)股數(shù)據(jù)和指數(shù)數(shù)據(jù)進(jìn)行實(shí)證研究。個(gè)股數(shù)據(jù)選取浦發(fā)銀行和平安銀行2012年11-12月逐筆交易的超高頻數(shù)據(jù),指數(shù)數(shù)據(jù)選取上證綜指和深證成指2011年和2012年兩年的每分鐘高頻數(shù)據(jù)。實(shí)證研究表明：中國(guó)股票市場(chǎng)的交易持續(xù)期和收益率的波動(dòng)性具有聚類(lèi)性的特征；交易持續(xù)期對(duì)股市波動(dòng)率有影響,持續(xù)期越大,波動(dòng)率越大；同時(shí)可以得出交易量對(duì)波動(dòng)率的影響更顯著,交易量越大,波動(dòng)率越大。說(shuō)明ACD-UHF-GARCH模型可以很好的反映高頻收益波動(dòng)聚類(lèi)性的本質(zhì),持續(xù)期和交易量對(duì)價(jià)格波動(dòng)有動(dòng)態(tài)影響。
[Abstract]:Under the condition of market economy, the stock market in China is more mature. At present, the volatility of stock market has been a hot topic in financial research. At present, many scholars have studied the relationship between volume and price of stock market. However, the time factor is rarely introduced, so the transaction volume and the time factor are included in the model. Explain the role of duration and trading volume in price volatility. The use of high-frequency data to study stock market volatility is essentially the use of trading time intervals to measure volatility. The high-frequency time series contains information about the market's microstructure. And important information on long-term daytime phenomena. This paper is based on previous studies. To collect the data of Shanghai and Shenzhen stock markets, we choose the ACD model to study the high frequency data and the GARCH model to measure the volatility of the stock market by using the low frequency data. The ACD model is used to calculate the average transaction duration, and then the GARCH model to deal with UHF data is constructed by using the parameters. That is, UHF-GARCH model. When establishing UHF-GARCH model, this paper not only takes into account the duration of the transaction. At the same time, considering the influence of trading volume on the stock market return and volatility. This paper selects the data of individual stocks and index data to carry on the empirical research. The data of individual stocks are selected from Pudong Development Bank and Ping an Bank on 2012 11-1. UHF data traded on a case-by-case basis in February. The index data are selected from the Shanghai Composite Index and Shenzhen Composite Index on 2011 and 2012. The empirical study shows that:. The volatility of trading duration and yield in Chinese stock market is characterized by clustering. Trading duration has an impact on the volatility of the stock market, the longer the duration, the greater the volatility; At the same time, it can be concluded that the effect of trading volume on volatility is more significant, and the greater the volume, the greater volatility. It shows that ACD-UHF-GARCH model can well reflect the nature of high frequency return volatility clustering. Duration and trading volume have a dynamic impact on price volatility.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：F832.51;F224

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