本文關(guān)鍵詞：基于異質(zhì)市場假說超高頻時間序列建模及應(yīng)用研究　出處：《長沙理工大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 超高頻數(shù)據(jù) HAR-BACD-V模型 市場微觀結(jié)構(gòu) 量價關(guān)系

【摘要】：股市波動及相關(guān)特征是國內(nèi)外學(xué)者研究金融衍生工具的定價、有效資產(chǎn)組合的選擇及金融風(fēng)險管理的關(guān)鍵變量，也是金融學(xué)領(lǐng)域的一個研究熱點。近年來，隨著計算機及通訊技術(shù)的快速發(fā)展，極大的降低數(shù)據(jù)記錄和存儲的成本，從而使得金融高頻數(shù)據(jù)日益成為研究金融資產(chǎn)價格波動及市場微觀結(jié)構(gòu)的重要研究對象，同時，也掀起了國內(nèi)外學(xué)者研究金融高頻時間序列的熱潮。自Engle和Russell（1998）首次提出自回歸條件久期模型（Autoregressive Conditional DurationACD）以及Anderson和Bollerslev(1998)首次提出“已實現(xiàn)”波動率的概念以來，基于高頻日內(nèi)數(shù)據(jù)的建模取得了顯著發(fā)展。 首先，在金融高頻時間序列基本特征認識的基礎(chǔ)上，本文對金融高頻/超高頻時間序列的建模問題作了深入探討，著重對基于高頻數(shù)據(jù)的HAR-RV模型和基于超高頻數(shù)據(jù)的ACD模型從理論推導(dǎo)和參數(shù)估計兩方面做了詳細介紹。 其次，，本文從高頻時間序列的定義和基本統(tǒng)計特征研究出發(fā)，重點從理論上對高頻金融數(shù)據(jù)的基本統(tǒng)計特征進行了歸納和總結(jié)，然后利用中國上證綜指等時1分鐘高頻數(shù)據(jù)對中國股市上的高頻時間序列的基本統(tǒng)計性質(zhì)進行了實證分析，研究發(fā)現(xiàn)我國股市高頻數(shù)據(jù)在統(tǒng)計上表現(xiàn)出明顯的“尖峰厚尾”的非正態(tài)特征，且“日內(nèi)效應(yīng)”十分顯著。 最后，從異質(zhì)市場假說的角度，基于超高頻時間序列構(gòu)建了HAR-BACD-V模型，并將其應(yīng)用在中國股市上進行實證分析。實證結(jié)果進一步驗證了我國股市交易者的異質(zhì)性，且不同交易頻率的投資者的交易行為對我國股市波動的影響是不同的：即短期交易者對股市波動影響最大，中期交易者其次，長期交易者影響最�。涣硗�，還發(fā)現(xiàn)微觀因子交易量對交易持續(xù)期存在明顯的負向效應(yīng)，這從實證的角度進一步驗證了Easley和O’Hara (1992)的市場微觀結(jié)構(gòu)假說，同時交易量對股市收益率和波動率也有較強的正向影響，這也說明了我國股市的信息傳播基本上是符合Copeland（1976）的連續(xù)信息到達假說。
[Abstract]:Stock market volatility and its related characteristics are the key variables of financial derivatives pricing, effective portfolio selection and financial risk management, and are also a hot research topic in the field of finance. With the rapid development of computer and communication technology, the cost of data recording and storage is greatly reduced. As a result, high-frequency financial data has become an important research object in the study of financial asset price volatility and market microstructure. Since Engle and Russell 1998), the autoregressive conditional duration model was first put forward (. (Autoregressive Conditional DurationACD) and Anderson and Bollerslev 1998). The concept of "realized" volatility has been proposed for the first time. Modeling based on high frequency day data has made remarkable progress. Firstly, based on the recognition of the basic characteristics of financial high-frequency time series, the modeling of financial high-frequency / ultra-high frequency time series is deeply discussed in this paper. The HAR-RV model based on high frequency data and the ACD model based on UHF data are introduced in detail from two aspects: theoretical derivation and parameter estimation. Secondly, from the definition of high-frequency time series and the study of basic statistical characteristics, this paper focuses on the theoretical analysis of the basic statistical characteristics of high-frequency financial data. Then the statistical properties of the high frequency time series in the Chinese stock market are analyzed empirically by using the high frequency data of 1 minute isochronous time series of the Shanghai Composite Index of China. It is found that the high frequency data of Chinese stock market show obvious non-normal characteristics of "peak and thick tail" statistically, and the "intraday effect" is very significant. Finally, from the perspective of heterogeneous market hypothesis, the HAR-BACD-V model is constructed based on UHF time series. The empirical results further verify the heterogeneity of Chinese stock market traders. The trading behavior of investors with different trading frequencies has different effects on the volatility of China's stock market: the short-term traders have the greatest impact on the stock market volatility, the medium-term traders have the second, and the long-term traders have the least impact; In addition, it is also found that the transaction volume of micro factors has a negative effect on the transaction duration. This further verifies the market microstructure hypothesis of Easley and Ogan Hara 1992 from the empirical point of view, and the trading volume also has a strong positive effect on the stock market return and volatility. This also shows that the information transmission in China's stock market is basically consistent with the hypothesis of continuous information arrival proposed by Copelandin1976.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F832.51;F224

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