本文關(guān)鍵詞：基于加權(quán)已實(shí)現(xiàn)極差的中國(guó)股市波動(dòng)特征研究　出處：《長(zhǎng)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： 已實(shí)現(xiàn)極差 加權(quán)已實(shí)現(xiàn)極 非對(duì)稱性 交易量

【摘要】：隨著計(jì)算機(jī)及通信技術(shù)的發(fā)展，當(dāng)獲取金融高頻數(shù)據(jù)成為可能后，，如何運(yùn)用高頻數(shù)據(jù)進(jìn)行建模并估計(jì)波動(dòng)率成為當(dāng)今研究的熱點(diǎn)問(wèn)題之一。已實(shí)現(xiàn)極差波動(dòng)是針對(duì)高頻時(shí)間序列而提出的一種全新的波動(dòng)率度量方法，而加權(quán)已實(shí)現(xiàn)極差可以有效的去除波動(dòng)的“日內(nèi)效應(yīng)”，是比已實(shí)現(xiàn)極差更有效的波動(dòng)估計(jì)量。 一般而言，不同的股票市場(chǎng)表現(xiàn)出不同的波動(dòng)特征，波動(dòng)率估計(jì)量的優(yōu)劣性在于能否精確地刻畫出股票市場(chǎng)波動(dòng)的典型特征及變化趨勢(shì)。大量研究表明，中國(guó)股票市場(chǎng)的波動(dòng)呈現(xiàn)出尖峰厚尾，自相關(guān)性和非對(duì)稱等特征，并且容易受交易量、價(jià)差等市場(chǎng)微觀因子的影響，基于中國(guó)股市高頻數(shù)據(jù)構(gòu)造出的加權(quán)已實(shí)現(xiàn)極差是否也能夠刻畫出上述的波動(dòng)特征，目前還沒(méi)有相關(guān)文獻(xiàn)對(duì)此進(jìn)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(shí)現(xiàn)波動(dòng)、已實(shí)現(xiàn)極差和加權(quán)已實(shí)現(xiàn)極差序列，通過(guò)理論和實(shí)證比較分析，已實(shí)現(xiàn)極差序列的方差是已實(shí)現(xiàn)波動(dòng)序列方差的五分之一，加權(quán)已實(shí)現(xiàn)極差可以有效的去處“日內(nèi)效應(yīng)”，且具有更穩(wěn)定的序列特征，證明了基于中國(guó)股市高頻數(shù)據(jù)加權(quán)已實(shí)現(xiàn)極差為更有效的波動(dòng)率估計(jì)量。 隨后，為了全面考察加權(quán)已實(shí)現(xiàn)極差波動(dòng)的特征和預(yù)測(cè)未來(lái)波動(dòng)，本文對(duì)加權(quán)已實(shí)現(xiàn)極差進(jìn)行建模，通過(guò)分析其序列性質(zhì)，本文運(yùn)用自回歸模型（AR模型）對(duì)其對(duì)數(shù)序列進(jìn)行建模，并在該模型的基礎(chǔ)上分別添加非對(duì)稱變量及交易量因素，研究加權(quán)已實(shí)現(xiàn)極差序列的非對(duì)稱特征以及交易量對(duì)其的影響。實(shí)證結(jié)果表明：中國(guó)股票市場(chǎng)上加權(quán)已實(shí)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(duì)稱及交易量因素后，模型的預(yù)測(cè)能力增強(qiáng)，表明加權(quán)已實(shí)現(xiàn)極差具有非對(duì)稱性特征，并且與交易量存在較強(qiáng)的正相關(guān)關(guān)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(xué)位授予單位】：長(zhǎng)沙理工大學(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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