金融計(jì)量-GARCH模型在金融大數(shù)據(jù)中地的應(yīng)用

實(shí)驗(yàn)報(bào)告七 模型在金融數(shù)據(jù)中的應(yīng)用一.實(shí)驗(yàn)?zāi)康睦斫庾曰貧w異方差（ ）模型的概念及建立的必要性和適用的場(chǎng)合。了解（） 模型的各種不同類(lèi)型，如 模型， 模型和模型。掌握對(duì)（） 模型的識(shí)別、估計(jì)及如何運(yùn)用 軟件在實(shí)證研究中實(shí)現(xiàn)。二.實(shí)驗(yàn)步驟一）滬深股市收益率的波動(dòng)性研究描述性統(tǒng)計(jì)數(shù)據(jù)選取與導(dǎo)入本實(shí)驗(yàn)選取中國(guó)上海證券市場(chǎng)股成分指數(shù)上證 和深圳證券市場(chǎng)股成分指數(shù)深證30作0為研究對(duì)象。分別從財(cái)經(jīng)網(wǎng)站上下載了201年05月4號(hào)到20年4月19號(hào)這將近6年的上證18和0深證30的0每日收盤(pán)價(jià)，共144個(gè)8。其中，上證指數(shù)的日收盤(pán)價(jià)以下記為，深證 指數(shù)的日收盤(pán)價(jià)以下記為。將下載的數(shù)據(jù)導(dǎo)入 。生成收益率的數(shù)據(jù)列”，生成上證”，生成深證”，生成上證”，生成深證指數(shù)的日收益率序列，記為,輸入“指數(shù)的日收益率序列，記為。觀察收益率的描述性統(tǒng)計(jì)量利用作出的滬市收益率的描述性統(tǒng)計(jì)量如圖所示。

利用作出的滬市收益率的描述性統(tǒng)計(jì)量如圖所示。圖滬市收益率的描述性統(tǒng)計(jì)量從上圖可以看出，樣本期內(nèi)，滬市收益率的均值為0.00，3標(biāo)9準(zhǔn)5差%為1.66，偏6度9為%-0.66，左8偏2峰0度1為7.31，6遠(yuǎn)6高8于3正態(tài)分布的峰度值3，說(shuō)明滬市收益率具有尖峰和厚尾特征。統(tǒng)計(jì)量為 ，說(shuō)明在極小水平下，滬市收益率顯著異于正態(tài)分布。利用 作出的深市收益率的描述性統(tǒng)計(jì)量如圖所示。圖深市收益率的描述性統(tǒng)計(jì)量從上圖可以看出，樣本期內(nèi)，深市收益率的均值為0.01，2標(biāo)8準(zhǔn)%差為1.79，偏2度6為%-0.78，左1偏0峰0度7為6.07，9遠(yuǎn)5高5于7正態(tài)分布的峰度值3，說(shuō)明深市收益率也具有尖峰和厚尾特征。統(tǒng)計(jì)量為 ，說(shuō)明在極小水平下，滬市收益率也顯著異于正態(tài)分布。而且深市收益率的標(biāo)準(zhǔn)差略大于滬市，說(shuō)明深市的波動(dòng)性更大。平穩(wěn)性檢驗(yàn)利用 軟件對(duì)和進(jìn)行平穩(wěn)性檢驗(yàn)。滬市收益率的檢驗(yàn)結(jié)果如圖所示；深市收益率的 檢驗(yàn)結(jié)果如圖所示。

NullHypothesis:RHhasaunitrootExogenous:ConstantLagLength:0(Automatic-basedonSIC,maxlag=10;t-StatisticProb.*AugmentedDickey-Fullerteststatistic-37.042590.0000Testcriticalvalues: 1%level-3.43466斗5%level-2.86333310%level-2.567773^MacKinnon(1996)one-sidedp-values.AugmentedDickey-FullerTestEquationDependentVariable:DfRH)Method:LeastSquares□ate:05/11/16Time:20:54Sample(adjusted}:31448Includedobservations:1446afteradjustmentsVariableCoefficientStd.Errort-StatisticProL.-0.9744620.026307-37.042590.0000C3.54E-050.0004390.0006240.9358R-squared0.487244Meandlependentvar-3.18E-06AdjustedR-squared0.406889S.D.dependentvar0.023279S.E.ofregression0.016675Akaikeinfocriterion-5.348441Sumsquaredresid0.401508Schwarzcriterion-5.341143Loglikelihood3S6S.923Hannan-Quinncriter.-5.345717F-statistic1372.154□urbin-Watsonstat1.991214Prob(F-statistic)0.000000圖 的檢驗(yàn)結(jié)果NullHypothesis:RZhasaunitrootExogenous:ConstantLagLength:0(Automatic-basedonSIC,maxlag=10)t-StatisticProb/AugmentedDickey-Fullerteststatistic35.685690.0000Testcriticalvalues: 1%level-34346645%level-2.863S3310%level-2.567773^MacKinnon(1996)one-sidedp-values.AugmentedDickey-FullerTestEquationDependentVariable:D(RZ}Method:LeastSquares□ate:05^11/16Time:20:54Sample(adjusted}:31443Includedobservations:1446afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.-0.9369900.026257-35.685690.0000C0.0001090.0004710.231174-0.8172R-squared0.463623Meandependentvar-1.20E-05AdjustedR-squared0.46B255S.D.dependentvar0.024545S.E.ofregression0.017S98Akaikeinfocriterion-5.206053Sumisquaredresid0.462578Schwarzcriterion-5.199555Loglikelihood3766.555Hannan-Quinncriter.-5.204-129F-statistic1273.469Durbin-Watsonstat1.907091Prob(F-statistic)0.000000圖 的 檢驗(yàn)結(jié)果從這兩個(gè)檢驗(yàn)結(jié)果可以看出，和的 檢驗(yàn)值都小于臨界值，說(shuō)明滬市收益率和深市收益率都是平穩(wěn)的。均值方程的確定及殘差序列自相關(guān)檢驗(yàn)通過(guò)對(duì)收益率的自相關(guān)檢驗(yàn)，可以發(fā)現(xiàn)滬市的收益率與其滯后7階存在顯著的自相關(guān)，而深市的收益率也與其滯后7階存在顯著的自相關(guān)，因此建立的均值方程如下：對(duì)收益率做自回歸利用普通最小二乘法對(duì)和做回歸，回歸結(jié)果如圖所示。利用普通最小二乘法對(duì)和做回歸，回歸結(jié)果如圖所示。DependentVariable:RHMethod:LeastSquares□ate:05/14^16Time:17:34Sample(adjusted}:91448Includedobservations:1440afteradjustmentsVariableCoefficientStd.Errort-StatisticProt).C6.72E-050.0004380.1535290.87S0RH(-7}0.0478700.0262351.8246560.0683R-squared0.002310Meandependentvar6.36E-05AdjustedR-squared0.001616S.D.dependentvar0.016631S.E.ofregression0.016618Akaikeinfocriterion-5.355294Sumsquaredresid0397107Schwarzcriterion-5.347971Loglikelihood3857.812Hannan-Quinncriter.-5.352560F-statistic3.329370□urbin-Watsonstat1.945953Prob(F-statistic)0.063260圖收益率的回歸結(jié)果忽略常數(shù)項(xiàng)的不顯著，的均值方程估計(jì)為再對(duì)和 做回歸，回歸結(jié)果如圖所示。DependentVariable:RZMethod:LeastSquares□ate:05^14^16Time:17:37Sample(adjusted):9t斗4日Includedobservations:1440afteradjustmentsVariable (CoefficientStd.Error t-StatisticProb.C0.0001590.000471 0.3336320.7349RZ(-7)0.0610250.026236 2.3259630.0202R-squareo0.003743Meandependentvar0.000167AdjustedR-squared0.003055S.D.dependentvar0.017095S.E.ofregression0.017367Akaikeinfocriterion-5.210315Sumsquaredresid0.459061Schwarzcriterion-5.202993Loglikelihood3753.427Hannan-Quinncriter.-5.207582F-statistic5.410102Durbin-Watsonstat1.875428Prob(F-statistic)0.020159圖收益率的回歸結(jié)果同樣忽略常數(shù)項(xiàng)的不顯著，的均值方程估計(jì)為用 統(tǒng)計(jì)量對(duì)均值方程擬合后的殘差及殘差平方做自相關(guān)檢驗(yàn)

□ate:05/14^16Time:17:42Sample:91448Includedobservations:1440AutocorrelationPartialCorrelationAC PACQ-StatProbI11110.0270.0271.04000.306(1112-0.037-0.0333.00030.223I1113-0.00B-0.0063.08370.379I]1]40.07B0.07511.4160.022I11150.0150.01011.7390.039匚1L16-0.094--0.09024.5890.000I1111-0.0040.00324.6150.001I]1]S0.0530.04829.592O.DDOI11190.03E0.03031.4480.000(11110-0.025-0.01132.3420.000(1(111-0.043-0.03735.0090.000I111120.0210.00735.6250.000I]1]130.0730.06242.7760.000[1[114-0.073-0.07351.S440.000I111150.004-0.02351.S640.000圖 殘差的自相關(guān)系數(shù)和值偏自相關(guān)系數(shù)顯示殘差不存在顯著的自相關(guān)。再得到殘差平方的自相關(guān)系數(shù)和值，如圖所示。Dafte:05/14/16Time:1'7:MSample:91448includedobservations:144QAutocorrelationPartialCorrelationAC PACO-StatProbi□i□10.1800.18046.9390.000ii□20.2370.212125.360.000ii□30.2120.152191620.000iZli]』0.19J0.097242.400.000i□i]50.1500.051275.170.000i□i160.1240.023297.270.000i□i170.1350.0^1632S.520.000iZli130.1040.019339.170.000i1i190.09-0.010351.250.000i□i]100.12E0.06037030.000iJi1110.09-0.018386.860.000iJi1120.0920.017399.140.000i□in130.1790.119445.740.000i1i1140.0960.011459.090.000iJi1150.034-0.015469.330.000圖偏自相關(guān)系數(shù)顯示再做出殘差和圖偏自相關(guān)系數(shù)顯示再做出殘差和所0示。殘差平方存在顯著的自相關(guān)。所0示。殘差平方的自相關(guān)系數(shù)圖，如圖和圖□ate:05/14/16Time:1746Sample:9144SIncludedobservations:*1440AutocorrelationPalialCorrelationACRACQ-StatProb1]1]10.0620.0625.59610.018(1(12-0.041-0.045799520.018111130.0150.021833470.04011140.0290.0259.53770.049111150.0160.015993120.077(1116-0.042-0.04312.5140.05111117-0.0040.00212.5410.0041]190.0440.04015.3290.0531111g0.0120.00715.5490.077[1[110-0.064-0.06121.5620.017ii11ii0.0090.01921.5S50.027111112-0.002-0.01321.6950.0411]1]130.0700.07320.3470.007(1[114-0.047-0.05232.0030.0041111150.0070.02332.0830.006圖 殘差的自相關(guān)系數(shù) 和值□ate:05^14^16Time:17:47Sample:914-4-8Includedobservations:1440AutocorrelationPartialCorrelationAC 3ACQ-S1atProbI■I□10.2073.20761.6320.000IIZU20.272D.240163.760.000IzzI□30.239D.162250.970.000IZlIZl40.209D.101313.980.000I□I50.200D.0S2372070.000I□II60.133D.002397.510.000I□II70.121D.000413.880.000I□I]80.147D.053450.300.000I□I190.126D.037473.330.000I□I100.176D.094513.360.000I□I]110.148D.054550.170.000I□I1120.103-D.016565.570.000IZlIZl130.207D.100627.720.000I□IJ140.1903.0906S0.440.000I□I1150.119-D.022700.950.000圖 殘差平方的自相關(guān)系數(shù)和值從圖中可以得到與類(lèi)似的結(jié)論，即的殘差不存在顯著的自相關(guān)，而殘差平方存在顯著的自相關(guān)。對(duì)殘差平方做線性圖對(duì)進(jìn)行回歸后提取殘差，生成殘差平方序列 ；對(duì)進(jìn)行回歸后提取殘差，生成殘差平方序列 。利用軟件作出 和 的線形圖，如圖和圖所示。

由這兩個(gè)圖可以看出，￡的波動(dòng)具有明顯的時(shí)間可變性和集簇性，比如在50和0附近比較小，也就是說(shuō)適合用類(lèi)模型來(lái)建模。對(duì)殘差進(jìn)行對(duì)做回歸之后的窗口中進(jìn)行s選擇一階滯后，得到檢驗(yàn)結(jié)果如圖所示。同樣步驟得到的檢驗(yàn)結(jié)果，如圖 所示。HeteroskedasticityTest:ARCHF-statistic 40.32412ProtJ.F(1.1437) 0.0000Obs^R-squared 斗6.S1700Prob.Chi-Squane(1) 0.0000TestEquation:□ependgm'Vmrimtiie:resideMethod:LeastSquares□ate:05/15/16Time:09:42Sample(adjusted}:131448Includedobservations:1439afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C0.0002261.94E-0511.652620.0000RESIDA2(-1}0.1803770.0259436.9515550.0000R-squared0.03^53dMeandep@ndertwmr0.000276AdjustedR-squared0.031861S.D.dlependentvar0.000695S.E.ofregression0.000634Akaikeinfocriterion-11.73512Sumsquaredresid0.000673Schwarzcriterion-11.727S0Loglikelihood6445422Hannan-Quinncriter.-11.73239F-statistic48.32412□urbin-WatsonEtat2.066934Prob(F-statistic)0.000000圖HeteroskedasticityTest:ARCHF-statistic 64.13170Prot.F(1.1437) 0.0000Obs^R-squared 61.4-7729Prob.Chi-Square(l) 0.0000TestEquation:DependentVariable:RESIDA2Method:LeastSquares□ate:05/15^16Time:09:43sample(adjuseed):101斗斗8Includedobservations:1439afteradjustmentsVariableCoefficientStdErrort-StatisticProb.c0.0D02532.04E-0512.427040.0000RESIDA2-1}0.2D6694-0.025810S.0002270.0000R-squared0.042722閘e日ndependEntvar0.000319AdjustedR-squa.red0.042056S.D.dependentvar0.000722S.E.ofregression0.0D0707Akaikeinfocriterion-11.67097Sumsquarednesid0.0D0717Schwaizcriterion-1166364Loglikelihood8399.259Hannan-Quinncriter.-11.66823F-statistic64.13170□urbin-Watsonstat2.080575Prob(F-statistic}0.0D0000圖14rzARCH-LMTest檢驗(yàn)的原假設(shè)是殘差中一直到第階都沒(méi)有 現(xiàn)象。在這里 由檢驗(yàn)結(jié)果可以看出，的檢驗(yàn)統(tǒng)計(jì)量和檢驗(yàn)統(tǒng)計(jì)量都大于臨界值,因此拒絕原假設(shè)，認(rèn)為殘差中， 效應(yīng)是顯著的。對(duì)于來(lái)說(shuō)也是這樣，殘差中的 效應(yīng)也顯著。

類(lèi)模型建模模（型1估,計(jì)1結(jié)）果對(duì)和分別進(jìn)行 建模。其均值方程形式為P其中表示和都可以。其條件方差方程為h h利用軟件對(duì)進(jìn)行估計(jì)，估計(jì)結(jié)果如圖所利用軟件對(duì)進(jìn)行估計(jì)，估計(jì)結(jié)果如圖所5示?！鮡pendentVariable:RHMethod:ML-ARCH(Marquardt}-Normaldistribution□ate:05^15/16Time:09:59Sample(adjusted}:91448Includedobservations:1440afteradjustmentsConvergenceachievedafter10iterationsPresamplevariance:backcast(parameter=0.7}GARCH二C(3)+C(4rRESID(-1尸2+C(5)*GARCH(-1;R-squaredAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-Watsonstat0.0022420.0015480.0166-10R-squaredAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-Watsonstat0.0022420.0015480.0166-100.3971343998.9681.945433MeandependentvmrS.D.dependentva.rAkaikeinfocriterionSchwarzcriterionHmnnan-Quinncriter.6.86E-050.016631-5.547177-5.528870-5.540343VariableCoefficientStdError^StatisticProb.C9.57E-050.0003750.25&4530.7934RH-7}0.0559120.0256302.1772580.0295VarianceEquationC3.45E-06S.54E-074.0345070.0001RESIDM^0.0530780.00679578114190.0000GARCH0.9331620.007601122.77630.0000圖 的 ，模型估計(jì)結(jié)果由估計(jì)結(jié)果可以看出，估計(jì)的模型為h h此外，除常數(shù)項(xiàng)外其他各系數(shù)全部顯著，說(shuō)明序列具有顯著的波動(dòng)集簇性。而且項(xiàng)和 項(xiàng)系數(shù)之和為 ，小于，也符合理論。因此對(duì)建立的 ，模型是平穩(wěn)的，其條件方差表現(xiàn)出均值回復(fù)，即過(guò)去的波動(dòng)對(duì)未來(lái)的影響是逐漸衰減的。再對(duì)進(jìn)行建模，估計(jì)結(jié)果如圖所示。

DependentVariable:RZMethod:ML-ARCH(Marquardt)-Normaldistribution□ate:05/15/16Time:10:07Sample(adjusted；:91448Includedobservations:1440afteradjustmentsConvergenceachievedafter15iterationsPresamplevarianc?:backcast(parameter=0.7)GARCH=C(3)+C(4rRESID(-1尸2+C(5)*GARCH[-1}R-squareaAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-Watsonstat0.0036990.0030060.017B68R-squareaAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-Watsonstat0.0036990.0030060.017B680.4590343903.2771.875473MeandependentvarS.D.dependentvarAkaikeinfocriterionSchwarzcriterionHannmn-Quinncriter.0.0001670.017S95-5.414273-5.395966-5.407439,模型估計(jì)結(jié)果估計(jì)的模型為對(duì)的數(shù)全部顯著，說(shuō)明,模型估計(jì)結(jié)果估計(jì)的模型為對(duì)的數(shù)全部顯著，說(shuō)明,模型的估計(jì)結(jié)果分析與類(lèi)似,序列具有顯著的波動(dòng)集簇性。而且除常數(shù)項(xiàng)外其他各系項(xiàng)和項(xiàng)系數(shù)之和為，小于，也符合理論。因此對(duì)建立的）模之和為，小于，也符合理論。因此對(duì)建立的）模1型是平穩(wěn)VariableCoefficientStd.Errorz?StatisticProb.C0.0002060.0003940.5218740.6018RZ(-7}0.0675310.0269112.5094500.0121VarianceEquationC3.20E-069.05E-073.5395100.0004-RESID(-1^20.0407090.0060917.0686370.0000GARCH(-1)0.93974-70.007006134.13220.0000的,其條件方差表現(xiàn)出均值回復(fù),即過(guò)去的波動(dòng)對(duì)未來(lái)的影響是逐漸衰減的。估計(jì)結(jié)果對(duì)進(jìn)行 模型估計(jì)，在 項(xiàng)中選擇方差，得到的模型估計(jì)結(jié)果如圖所示。

DependentVariable:RHMethod:ML-ARCH(Marquardtj-NormaldistributionDependentVariable:RHSample(adjusteal):91448l「icli」dWdobservations；:1440afteradjustmentsConvergenceachievedafter24iterationsVariableCoefficientStd.Errorz?StatisticProb.GARCH0.1965203.0718580.0639740.9490VariableCoefficientStd.Errorz?StatisticProb.GARCH0.1965203.0718580.0639740.9490C5.53E-050.0007120.0777570.93S0RH(-7}0.0559630.0256332.1789780.0293VarianceEquationC3.45E-068.58E-074.0202610.0001RESID(-1P2O0531140.0063467.7&87450.0000GARCH[-1}0.9331180.007605122.70090.0000R-squaredAdjustedR-squaredMeandependentvarS.D.dlependE「itHaL「Presamplevariance:backcastiparameter=0.71S.E.ofregressionSumsquaredresidLoglikelihood由估計(jì)結(jié)果可以看出，均值方程中的 項(xiàng)的系數(shù)并不顯著，說(shuō)明 并不適合用模型來(lái)進(jìn)行估計(jì)。同樣步驟得到的 模型估計(jì)結(jié)果，如圖所示。DependentVariable:RZMethod:ML-ARCH(Marquardt}-Normaldistribution□ate:05^15^16Time:10:17Sample(adjusted}:914-4-3Includedobservations:1440afteradjustmentsConvergenceachievedafter28iterationsPresampievariance:backcast(parameter=0.7)GARCH=C(4;C(5fRESID(-1f2C(6)*GARCHf-1}VariableCoefficientStd.Errorz>StatisticProb.GARCH1.4729272.3733400.5117280.6088C-0.0001300.000752-01735070.3623RZf-7)0.0602360.026874-2.5409460.0111VarianceEquationC3.25E-069.64E-07 3.3752020.0007RESID(-1^20.0492230.007125 6.9087990.0000GARCH(-1)0.9390390.007123 131.74540.0000R-squa.red0.002493Meandependent畑0.000167AdjustedR-squared0.001105S.D.dependentvar0.017B95S.E.ofregression0.017835Akaikeinfocriterion-5.413005Sumisquaredresid0.^159640Schwarzcriterion-5.391117Loglikelihood3903.421Hannan-Quinncriter.-5.404085Durbin-Watsonstat1.871200模模（型估估計(jì)計(jì)估結(jié)）果

項(xiàng)的系的 模型估計(jì)結(jié)果與類(lèi)似，即均值方程中的項(xiàng)的系數(shù)并不顯著，說(shuō)明不適合用 模型來(lái)進(jìn)行估計(jì)。二果股市收益波動(dòng)非對(duì)稱(chēng)性的研究模型估計(jì)結(jié)果在所示。中填入1在所示。中填入1得到的，模型估計(jì)結(jié)果如圖DependentVariable'RHMethod:ML-ARCH(Marquardt)-NormaldistributionDate:05/15/16Time:10:23Sample(adjusted):9U48Includedobservations:144-0afterad.ustmentsCdmergeneeadiiei/edafter15iterationsPresamplevariance:tackcastfparameter=0.7)GARCH=0(3)+Q4FREEIDf-1尸2C(5FRESI□(-1^(RESI□(-1)0)-C(6FGARCH(-1)R-squaredAdjustedR-squaredS.E.regressionSumsquaredresidLoglikelihoodDurbin-Watscnstat0.002242R-squaredAdjustedR-squaredS.E.regressionSumsquaredresidLoglikelihoodDurbin-Watscnstat0.0022420.00154300166180.3971343990.9601.945434MeandependentvarS.D.dependentvarAkaikeinfocrierorSchwarzcriterionHannar-Quinncriter.6.B6E-050.016631-5.545789-5.523320-5.537583VariableCoefficientStd.ErrorZrStatisticProb.C9.60E-050.00038J0.2505810.3D21RHf-7)0.0553090.02569B2.1749620.0296VarianceEquationC3.44E-06SU53E-O74.033S670.DDO1RESID(-1f20.0531830.003309B.339660O.ODOORESID(-1^2*(RESID(-1)<0)-0.0002730.00916D-D.0297640.9763GARCH-1)0.9332330.007599122.SD68O.ODOO圖 的 ，模型估計(jì)結(jié)果估計(jì)結(jié)果顯示， 的系數(shù)估計(jì)值小于，并且不顯著，說(shuō)明在滬市中并不存在收益波動(dòng)的非對(duì)稱(chēng)性。同樣步驟得到的 ，模型估計(jì)結(jié)果如圖所示。

DependentVaiaUecRZMethod:ML-ARCH(Marquardt)-Normaldistribjtion□ate:05/15/16Tlrrne1026Sample(adjusted}:01449Includedobservations:1440afteradjustmentsConvergenceachievedafter16iteratonsPresamplevariance:backcast(parameter=0.7)GARCH= C(4)T尺ESIDL1『2十宣5嚴(yán)尺已8心曰嚴(yán)27尺ESID[-1卜⑴十C(6)*CARCH(-1}VariableCoefficien:Std.ErrorZrSlatisticProa.c0.35E-O50.0004090.2D42D40.0302RZ(-7)0.07220-10.026393273&6650.0062VarianceEquationc4.23E-061.06E-064.1D&7B00.0000RESIDMT20.0350250.0037014.0254720.0001RESIDt-ir2*(RE.SID(-1)<0'：0.0235400.0109232.6129420.0090GARCH(-1}0.9336600.007734120.7179U.0000R-squared0.003603Meandependentvar0.000167AdjustedR-squared0.002910S.D.dependentvar0.017895S.E.ofregression0.017863Akaikeinfocriterion-5.^115270Sumsquaredresld0.459123Schwarzcriterion-5.393302Loglikslihoodl3904.995HannanQuinncriter.-5.407069Durbin-Watsonstat1.S75496圖 的 ，模型估計(jì)結(jié)果估計(jì)結(jié)果顯示，的系數(shù)估計(jì)值大于）并且顯著，說(shuō)明在深市中存在收益波動(dòng)的非對(duì)稱(chēng)性，即壞消息引起的波動(dòng)比同等大小的好消息引起的波動(dòng)要大。模型估計(jì)結(jié)果對(duì)進(jìn)行估計(jì)，其估計(jì)結(jié)果如圖所示。對(duì)進(jìn)行估計(jì)，其估計(jì)結(jié)果如圖所示。DependentVariable:RHMethod:ML-ARCH(Marquardt)-Normaldistrioution□ate:DependentVariable:RHMethod:ML-ARCH(Marquardt)-Normaldistrioution□ate:05/15/16Time:10:29Sample(adjusted}:9144SIncludedobservations:*1440alteradjustmentsConvergenceachievedafter17iterationsPresamplevariance:backcast[parameter=0.7;LOG(GARCH)=C⑶+C(4)*ABS(RESID(-1J/@S€1RT(GARCH(-1}}}+C⑸^RESID(-1}/@SQRT(GARCH㈠"+C(6fLOG[GARCH(-1}}VariableCoefficientStdErrorz-SMsficProb.C9.38E-050.0003770.2437400.3036RH(-7>0.0515050.0249112.0675630.0337VarianceEquationc⑶-0.1997730.030940-6.4560080.0000C(4)0.1276400.0138209.2359540.0000U⑶-0.0D70120.007402-0.9472990.3435C(6)0.9B73420.003301299.05920.0000R-squaredAdjustedR-squareds.E.orregressionSumsquarednesidLoglikelihood□urbin-Watsorstat0.0D22940.0D16000.0166180.3S71133996.7481.945716MeandependentvarS.D.dependentvarAKalkeinfocriterionSchwaizcriterionHannan-Quinncriter.6.86E-050.016631-5.&42705-5.520737-5.534504-圖 的 ，模型估計(jì)結(jié)果估計(jì)結(jié)果中， 項(xiàng)的系數(shù) 為但是不能通過(guò)顯著性檢驗(yàn)，說(shuō)明滬市中不存在收益波動(dòng)的非對(duì)稱(chēng)性。同樣對(duì)進(jìn)行 ，模型估計(jì)，估計(jì)結(jié)果如圖所示。DependentVariable:RZMethod:ML-ARCH(Marquardt}-Normaldistribution□ate:05/15/16Time:1033Sample(adjusted}:91448Includedobservations:44斗0afteradjustmentsConvergenceachievedafter24iterationsPresamplevariance:backcast[parameter=0.7;LOGtGARCHCt3)+C(4rABS(RESIDt-1}/@SClRTtGARCH(-1D)+U⑸*RE.SID(-1)i/@SaRT[GARCH(-1})+C(6)*U3G(GARCH(-1}：■VariableCoefficientStd.Errorz?StatisticProb.C-7.26E-050.00D406-0.1788530.0581RZ(-7)0.0618540.0257432.4-022690.0163VarianceEquationG⑶-0.1941480.031U9 -&2329450.00000(4}0.1207010.014052 8.1269500.00000(5)-0.0266410.00B474 -3.1438780.0017C(6)0.9876630.003426 288.26110.0000R-squared0.003579Meandependentvar0.000167AdjustedR-squared0.002886S.D.dependentvar0.017895S.E.ofregression0.017869Akaikeinfocriterion-5.410352Sumsquaredresid0.459139Schwarzcriterion-5.383834Loglikelihood39D1.813Hannan-Quinncriter.-5.402651□urbin-Watsonstat1.S75119圖的, 模型估計(jì)結(jié)果估計(jì)結(jié)果中， 項(xiàng)的系數(shù)為并且通過(guò)了顯著性檢驗(yàn)，說(shuō)明深市中存在收益波動(dòng)的非對(duì)稱(chēng)性，這也與模型的估計(jì)結(jié)果相吻合。（三）滬深股市波動(dòng)溢出效應(yīng)的研究股市波動(dòng)的溢出效應(yīng)就是指不同資本市場(chǎng)之間波動(dòng)的傳遞，接下來(lái)進(jìn)行檢驗(yàn)深滬兩