發(fā)布時(shí)間：2018-01-04 18:29

  本文關(guān)鍵詞：股票收益率及系統(tǒng)性風(fēng)險(xiǎn)的估計(jì)研究　出處：《重慶大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 波動(dòng)性建模 DCC-MVGARCH模型 系統(tǒng)性風(fēng)險(xiǎn) 時(shí)變?chǔ)孪禂?shù)

【摘要】：文章主要討論的問(wèn)題是資本市場(chǎng)中股票收益率的波動(dòng)性建模以及系統(tǒng)風(fēng)險(xiǎn)系數(shù)的動(dòng)態(tài)估計(jì)。研究的兩個(gè)重點(diǎn)是股票收益率和系統(tǒng)風(fēng)險(xiǎn)系數(shù),收益率可以很好的衡量某個(gè)資本單日在前一日基礎(chǔ)上的收益情況,同時(shí)可以很好的衡量股票的波動(dòng)性。系統(tǒng)風(fēng)險(xiǎn)β系數(shù)衡量的是單個(gè)資本受到的市場(chǎng)的影響,反映證券的收益率水平對(duì)市場(chǎng)平均收益水平變化的敏感度,是衡量證券承擔(dān)系統(tǒng)風(fēng)險(xiǎn)水平的指標(biāo)。投資組合理論中的系統(tǒng)風(fēng)險(xiǎn)系數(shù)在理論研究以及投資實(shí)踐中都具有非常重要的作用,對(duì)它的研究將有效的為資產(chǎn)定價(jià)以及風(fēng)險(xiǎn)管理提供決策依據(jù)。文章的研究主要運(yùn)用的工具是時(shí)間序列分析方法中GARCH族模型和多元GARCH模型。金融時(shí)間序列一般情況下呈現(xiàn)出階段性的相對(duì)平穩(wěn)和階段性的劇烈波動(dòng),因此采用波動(dòng)性建模的ARCH模型族和GARCH模型族進(jìn)行估計(jì)。另外,在研究系統(tǒng)風(fēng)險(xiǎn)系數(shù)時(shí)需要估計(jì)單只股票與市場(chǎng)指數(shù)間的動(dòng)態(tài)條件相關(guān)系數(shù)序列,需要運(yùn)用多元GARCH模型,文章選取了DCC-MVGARCH兩步建模法實(shí)現(xiàn)了對(duì)動(dòng)態(tài)條件相關(guān)系數(shù)序列的估計(jì)。文章選取了上證50指數(shù)及其成分股的日線交易數(shù)據(jù)作為樣本,在完成對(duì)收益率數(shù)據(jù)的非正態(tài)性檢驗(yàn)、穩(wěn)定性檢驗(yàn)和ARCH效應(yīng)檢驗(yàn)的基礎(chǔ)上對(duì)數(shù)據(jù)進(jìn)行建模。在比較了模型的AIC、SIC、R平方和殘差平方和等指標(biāo)后確定了最優(yōu)的模型為GARCH模型族中適用于非對(duì)稱建模的EGARCH(2,2)模型,實(shí)現(xiàn)了對(duì)上證50指數(shù)收益率序列的良好擬合。最后文章收益率序列建模的基礎(chǔ)上討論了時(shí)變的系統(tǒng)性風(fēng)險(xiǎn)β系數(shù)。根據(jù)DCC-MVGARCH模型獲得成分股收益率與指數(shù)股收益率的動(dòng)態(tài)相關(guān)系數(shù)序列以及各自的條件方差序列,帶入模型βi=cov(ri,rn)/σm2從而得到系統(tǒng)風(fēng)險(xiǎn)系數(shù)序列。研究結(jié)果表明大多數(shù)成分股收益率的β系數(shù)都在0.5-1.5之間波動(dòng),且總體而言,系數(shù)在2015年二季度時(shí)波動(dòng)變小,這段時(shí)間各個(gè)股票走勢(shì)與大盤比較接近。所有成分股中,貴州茅臺(tái)的β系數(shù)數(shù)值較小且較為穩(wěn)定,是上證50指數(shù)板塊中風(fēng)險(xiǎn)最小的優(yōu)質(zhì)藍(lán)籌股,而招商證券的β系數(shù)較大,可能存在相當(dāng)高的風(fēng)險(xiǎn)。
[Abstract]:This paper mainly discusses the volatility model of stock return in capital market and the dynamic estimation of system risk coefficient. The two emphases of the study are stock return and systematic risk coefficient. The yield can well measure the return on a single capital day on the basis of the previous 1st, and can also measure the volatility of the stock. The systematic risk 尾 coefficient measures the impact of the market on a single capital. The sensitivity of the yield level of the securities to the change of the average return level of the market. The systematic risk coefficient in portfolio theory plays an important role in both theoretical research and investment practice. The main tools of this paper are GARCH family model and multivariate GARCH model in time series analysis. The time series generally show a relatively stable phase and violent fluctuations of the phase. Therefore, the volatility modeling ARCH model family and the GARCH model family are used to estimate the dynamic conditional correlation coefficient series between a single stock and the market index. Multivariate GARCH model is needed. This paper selects DCC-MVGARCH two-step modeling method to realize the estimation of dynamic conditional correlation coefficient series, and selects the daily trading data of Shanghai Stock Exchange 50 Index and its constituent stocks as samples. The data are modeled on the basis of non-normal test, stability test and ARCH effect test, and the AIC-SIC of the model is compared. After R squared sum and residual squared sum, the optimal model is EGARCH2 / 2) model which is suitable for asymmetric modeling in GARCH model family. The good fitting of the yield series of Shanghai 50 index is realized. Finally, the time-varying systematic risk 尾 coefficient is discussed on the basis of the model of return sequence. The components are obtained according to the DCC-MVGARCH model. The dynamic correlation coefficient series of stock yield and index stock return and their conditional variance series. A series of systematic risk coefficients are obtained by introducing the model 尾 -covrigne / 蟽 m ~ 2. The results show that the 尾 coefficients of most component stock returns fluctuate between 0.5-1.5. In general, the volatility of the coefficient in the second quarter of 2015 became smaller, during this period the trend of each stock is close to the market. Among all the constituent stocks, the 尾 coefficient of Moutai in Guizhou is smaller and more stable. It is the least risky high-quality blue chip in the Shanghai 50 index, while the 尾 coefficient of China Merchants Securities is large, which may have a high risk.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：F830.91;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 胡東濱;張展英;;基于DCC-GARCH模型的金屬期貨市場(chǎng)與外匯、貨幣市場(chǎng)的動(dòng)態(tài)相關(guān)性研究[J];數(shù)理統(tǒng)計(jì)與管理;2012年05期

2 鄭振龍;楊偉;;金融資產(chǎn)收益動(dòng)態(tài)相關(guān)性:基于DCC多元變量GARCH模型的實(shí)證研究[J];當(dāng)代財(cái)經(jīng);2012年07期

3 吳武清;陳敏;劉偉;;中國(guó)股市時(shí)變貝塔的統(tǒng)計(jì)特征及其在股指期貨中的應(yīng)用[J];系統(tǒng)工程理論與實(shí)踐;2008年10期

4 谷耀;陸麗娜;;滬、深、港股市信息溢出效應(yīng)與動(dòng)態(tài)相關(guān)性——基于DCC-(BV)EGARCH-VAR的檢驗(yàn)[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2006年08期

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本文編號(hào)：1379591