本文選題：上證新綜指 + 深證成指��； 參考：《南京大學(xué)》2017年碩士論文

【摘要】：在股市的價(jià)格變化中,波動(dòng)率是個(gè)十分重要的指標(biāo),也是許多國(guó)內(nèi)外學(xué)者研究的熱點(diǎn)問(wèn)題。不同發(fā)展水平的股票市場(chǎng)中均存在著波動(dòng)的不對(duì)稱(chēng)特征、方差時(shí)變特征和簇集特征,而且序列中的負(fù)向收益給股票價(jià)格帶來(lái)的波動(dòng)往往比正向收益來(lái)的大,說(shuō)明股票的價(jià)格序列中存在著杠桿效應(yīng)。與國(guó)外發(fā)達(dá)國(guó)家的股市相比,我國(guó)滬深股市的起步比較晚,發(fā)展還不夠成熟,股票市場(chǎng)的監(jiān)管措施還不那么完善,因此我國(guó)離發(fā)展成熟的股票市場(chǎng)還有很大差距。本文考慮了我國(guó)滬深股市的現(xiàn)狀,研究了滬深股市中價(jià)格的非對(duì)稱(chēng)特征和波動(dòng)特征,進(jìn)一步加深了我們對(duì)滬深股市波動(dòng)情況的了解。在我國(guó)滬深股市中,上證綜指和深證成指是兩種主要的指數(shù)。本文以上海證券交易所股權(quán)分置改革為起點(diǎn),以深圳證券交易所重新修訂深證成指編制方案為終點(diǎn),綜合運(yùn)用了描述性統(tǒng)計(jì)方法和基于G-ARMA-GARCH族模型的實(shí)證分析法分別對(duì)2006年1月4日至2015年5月19日兩種指數(shù)日收益率序列的波動(dòng)情況進(jìn)行了刻畫(huà),并對(duì)兩種指數(shù)進(jìn)行了對(duì)比。最后,根據(jù)擬合出的最佳模型分別預(yù)測(cè)了 2015年5月20日至2015年12月31日兩種指數(shù)的日收盤(pán)價(jià)數(shù)據(jù)。在描述性統(tǒng)計(jì)分析中,我們分別討論了兩種指數(shù)的正態(tài)性、平穩(wěn)性、異方差性、自相關(guān)性和偏自相關(guān)性。結(jié)果顯示上證新綜指和深證成指的日收益率序列均是平穩(wěn)序列,說(shuō)明可以用ARMA模型進(jìn)行序列條件均值的初步擬合。同時(shí)兩種指數(shù)的日收益率序列均存在左偏特征和尖峰厚尾特征,說(shuō)明序列可能存在異方差性,可以用GARCH族模型擬合序列的條件方差。在運(yùn)用G-ARMA-GARCH族模型進(jìn)行實(shí)證分析的過(guò)程中,考慮到當(dāng)日開(kāi)盤(pán)價(jià)與歷史收盤(pán)價(jià)之間可能存在一定的關(guān)系,我們?cè)贏RMA-GARCH族模型中加入了梯度因子來(lái)更好地反映歷史數(shù)據(jù)和隔夜跳空因素對(duì)序列波動(dòng)情況的影響。結(jié)果顯示GARCH族模型中的EGARCH模型比TGARCH模型更適合擬合上證新綜指和深證成指日收益率序列的條件方差,而且在序列的殘差項(xiàng)服從GED分布時(shí)擬合效果最好。此外,我們發(fā)現(xiàn)兩種指數(shù)的日收益率序列中均存在杠桿效應(yīng),且深證成指的杠桿效應(yīng)更強(qiáng)。同時(shí),深證成指日收益率序列條件方差的波動(dòng)幅度也比較大,說(shuō)明深證成指的風(fēng)險(xiǎn)水平更高。經(jīng)過(guò)比較傳統(tǒng)的ARMA-GARCH族模型和G-ARMA-GARCH族模型在預(yù)測(cè)日后收盤(pán)價(jià)時(shí)的精度差異,發(fā)現(xiàn)加入梯度因子后的模型預(yù)測(cè)的精度更高,穩(wěn)定性也更強(qiáng)。根據(jù)描述性統(tǒng)計(jì)分析和實(shí)證分析,我們可以發(fā)現(xiàn)我國(guó)滬深股市中還存在許多問(wèn)題,今后我們需要通過(guò)更深入的研究來(lái)改善這些問(wèn)題,以推動(dòng)我國(guó)滬深股市的進(jìn)一步發(fā)展。
[Abstract]:Volatility is a very important index in the price change of stock market, and it is also a hot issue studied by many scholars at home and abroad. In the stock market with different levels of development, there are asymmetric characteristics of volatility, time-varying variance and clustering characteristics, and the negative return in the series often brings more volatility to the stock price than the positive return. It shows that there is a leverage effect in the stock price sequence. Compared with the stock market of the developed countries, the stock market of Shanghai and Shenzhen started relatively late, the development of the stock market is not mature enough, and the supervision measures of the stock market are not so perfect, so there is still a big gap between our country and the mature stock market. This paper considers the present situation of Shanghai and Shenzhen stock markets, studies the asymmetric and fluctuating characteristics of prices in Shanghai and Shenzhen stock markets, and further deepens our understanding of the volatility of Shanghai and Shenzhen stock markets. In Shanghai and Shenzhen stock markets, Shanghai Composite Index and Shenzhen Composite Index are two main indexes. This paper starts with the reform of the split share structure of the Shanghai Stock Exchange, and ends with the Shenzhen Stock Exchange's revision of the Shenzhen Stock Exchange's plan for the compilation of the Shenzhen Stock Exchange's constituent index. In this paper, descriptive statistical method and empirical analysis method based on G-ARMA-GARCH family model are used to characterize the volatility of two kinds of index daily return series from January 4, 2006 to May 19, 2015, and to compare the two indices. Finally, the daily closing price data of the two indices from May 20, 2015 to December 31, 2015 are predicted according to the best fitting model. In descriptive statistical analysis, we discuss the normality, stationarity, heteroscedasticity, autocorrelation and partial autocorrelation of two indices. The results show that the daily yield series of Shanghai New Composite Index and Shenzhen Composite Index are stationary series, which indicates that the ARMA model can be used to preliminarily fit the conditional mean of the sequence. At the same time, the daily returns of the two indices have left bias and sharp peak and thick tail, which indicates that the sequence may have heteroscedasticity, and the conditional variance of the sequence can be fitted by GARCH family model. In the process of empirical analysis using G-ARMA-GARCH family model, considering that there may be a certain relationship between the opening price of the day and the historical closing price, The gradient factor is added to the ARMA-GARCH family model to better reflect the influence of historical data and overnight hopping factors on the fluctuation of the series. The results show that the EGARCH model in the GARCH family model is more suitable than the TGARCH model to fit the conditional variance of the daily return sequence of the Shanghai New Composite Index and the Shenzhen Composite Index, and the best fitting effect is obtained when the residual items of the series are distributed from GED. In addition, we find that there is a leverage effect in the daily yield series of the two indices, and the leverage effect of Shenzhen Composite Index is stronger. At the same time, the volatility of conditional variance in the daily yield series of Shenzhen Composite Index is also large, which indicates that the risk level of Shenzhen Composite Index is higher. By comparing the accuracy difference between the traditional ARMA-GARCH family model and the G-ARMA-GARCH family model in predicting the closing price in the future, it is found that the model with gradient factor has higher accuracy and stronger stability. According to descriptive statistical analysis and empirical analysis, we can find that there are still many problems in China's Shanghai and Shenzhen stock markets. In the future, we need to improve these problems through more in-depth research in order to promote the further development of China's Shanghai and Shenzhen stock markets.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F224;F832.51
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本文編號(hào)：1900970