本文關鍵詞：基于加權已實現(xiàn)極差的中國股市波動特征研究　出處：《長沙理工大學》2012年碩士論文　論文類型：學位論文

  更多相關文章： 已實現(xiàn)極差 加權已實現(xiàn)極 非對稱性 交易量

【摘要】：隨著計算機及通信技術的發(fā)展，當獲取金融高頻數(shù)據(jù)成為可能后，，如何運用高頻數(shù)據(jù)進行建模并估計波動率成為當今研究的熱點問題之一。已實現(xiàn)極差波動是針對高頻時間序列而提出的一種全新的波動率度量方法，而加權已實現(xiàn)極差可以有效的去除波動的“日內(nèi)效應”，是比已實現(xiàn)極差更有效的波動估計量。 一般而言，不同的股票市場表現(xiàn)出不同的波動特征，波動率估計量的優(yōu)劣性在于能否精確地刻畫出股票市場波動的典型特征及變化趨勢。大量研究表明，中國股票市場的波動呈現(xiàn)出尖峰厚尾，自相關性和非對稱等特征，并且容易受交易量、價差等市場微觀因子的影響，基于中國股市高頻數(shù)據(jù)構(gòu)造出的加權已實現(xiàn)極差是否也能夠刻畫出上述的波動特征，目前還沒有相關文獻對此進行研究。 本文首先基于滬深300股指的五分鐘高頻數(shù)據(jù)構(gòu)造已實現(xiàn)波動、已實現(xiàn)極差和加權已實現(xiàn)極差序列，通過理論和實證比較分析，已實現(xiàn)極差序列的方差是已實現(xiàn)波動序列方差的五分之一，加權已實現(xiàn)極差可以有效的去處“日內(nèi)效應”，且具有更穩(wěn)定的序列特征，證明了基于中國股市高頻數(shù)據(jù)加權已實現(xiàn)極差為更有效的波動率估計量。 隨后，為了全面考察加權已實現(xiàn)極差波動的特征和預測未來波動，本文對加權已實現(xiàn)極差進行建模，通過分析其序列性質(zhì)，本文運用自回歸模型（AR模型）對其對數(shù)序列進行建模，并在該模型的基礎上分別添加非對稱變量及交易量因素，研究加權已實現(xiàn)極差序列的非對稱特征以及交易量對其的影響。實證結(jié)果表明：中國股票市場上加權已實現(xiàn)極差序列具有尖峰厚尾、集聚性、持續(xù)性等特征，在模型中加入非對稱及交易量因素后，模型的預測能力增強，表明加權已實現(xiàn)極差具有非對稱性特征，并且與交易量存在較強的正相關關系。
[Abstract]:With the development of computer and communication technology, when the acquisition of financial high frequency data as possible, how to use high frequency data modeling and estimation of volatility has become one of the hot issues of current research. The realized range based volatility is due to high frequency time series and proposed a new method to measure the volatility, and the weighted realized range can be effective remove the fluctuation of intra day effect, is more effective than the realized range based volatility estimators.
Generally speaking, different stock markets show different volatility features, volatility estimates of the amount of quality is the ability to accurately depict the typical characteristics and trend of fluctuations in the stock market. A large number of studies show that China the volatility of the stock market showing a peak thick tail, autocorrelation and asymmetric features, and is easily affected by the transaction the amount of price impact, micro market factor, weighted China stock market high frequency data structure of the realized range is also can be used to describe the volatility characteristics based on the above, there is no relevant literature to study.
Firstly, based on the CSI 300 index five minute high-frequency data structure realized volatility, realized range and weighted realized range sequence, through theoretical and empirical analysis, realized variance range sequence is realized volatility variance weighted 1/5, have been achieved range can effectively place intra day effect, and with the sequence characteristics of more stable, proved that the high frequency data China stock market based on weighted realized range rate estimation for more effective volatility.
Then, in order to fully investigate the weighted realized range based volatility characteristics and predict the future volatility, the weighted realized range based modeling, through the analysis of the sequence properties, using the autoregressive model (AR model) to model the logarithmic sequence, and add respectively factors asymmetric variables and trading volume on the basis of the model study, the weighted realized effect of asymmetric characteristics of poor sequence and the trading volume. The empirical results show that: China stock market weighted realized range based sequence has peak thick tail, agglomeration, characteristics of persistent, asymmetric add factors and trading volume in the model, to enhance the predictive ability of the models show that the weighted realized range is asymmetrical, and there is a strong correlation with trading volume.

【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F224;F832.51

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