實(shí)驗(yàn)14 Garch(自回歸異方差模型)

1、實(shí)驗(yàn)14G(ARCH)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(Autoregressiveconditionalheteroscedasticity)模型的概念及建立的必要性和適用的場(chǎng)合。了解G(ARCH)模型的各種不同類(lèi)型，如GARCH-M模型(GARCHmmean),EGARCH模型(ExponentialGARCH)和TARCH模型(又稱(chēng)GJR)。掌握對(duì)G(ARCH)模型的識(shí)別、估計(jì)即如何運(yùn)用Eviews軟件在實(shí)證研究中實(shí)現(xiàn)。二、實(shí)驗(yàn)內(nèi)容及要求內(nèi)容：以上證指數(shù)和深證成份指數(shù)為研究對(duì)彖，選取1997年1月2口到2002年12月31口共六年每個(gè)交易口上證指數(shù)和深證成份指數(shù)的收盤(pán)價(jià)

2、為樣本，完成以下實(shí)驗(yàn)步驟：、對(duì)滬深股市的收益率作波動(dòng)性研究、對(duì)股市收益波動(dòng)作非對(duì)稱(chēng)性的研究、對(duì)滬深股市作波動(dòng)溢出效應(yīng)研究要求：深刻理解本章的概念：對(duì)實(shí)驗(yàn)步驟中提出的問(wèn)題進(jìn)行思考；熟練掌握實(shí)驗(yàn)的操作步驟，并得到有關(guān)結(jié)果。三、實(shí)驗(yàn)指導(dǎo)、對(duì)滬深股市的收益率作波動(dòng)性研究描述性統(tǒng)計(jì)導(dǎo)入數(shù)據(jù)，建立工作組打開(kāi)Eviews軟件，選擇“File”菜單中的“NewWorkfile選項(xiàng)，在“Woikfilestructuretype框中選擇unstmctured/undated(思考：為什么用非規(guī)則形式)，在Daterange輸入1444,如下圖14-1：OOXiWorkfileCreate-DatarangeOb

3、servations:1444-Workfilestructuretype(unstructured/UndatedFIrregularDated3rdPanmlworkfilesmaybemadeFromUnstructuredworkfilesbylaterspecifyingdateand/orotheridentifierseries-Workfilenames(optional)WF:|HtestCancel圖14-1單擊OK，再在命令行輸入datashsz,把上證綜指和深幼I成指1997-1-2號(hào)到2002-12-31號(hào)數(shù)據(jù)輸入。生成收益率的數(shù)據(jù)列在Eviews窗II主菜單欄下得命

4、令窗II中鍵入如下命令：gemrh=log(slVsh(-1),回車(chē)后即形成滬市收益率的數(shù)據(jù)序列，同樣的方法可得深市收益數(shù)劇序列(gem-rz=log(sz/sz(-l)o新工作組如圖14-2：圖14-2觀察收益率sh的描述性統(tǒng)計(jì)量雙擊選取“rh數(shù)據(jù)序列，在出現(xiàn)的窗II中選擇view菜單下DescriptiveStatistics”菜單中的“HistogramandStats”子菜單，則可得收益率山的描述性統(tǒng)計(jì)量，如下圖7-3：sSeries:RHWorkfile:UNTITLED:Untitled-Inix|ViewProcObjectPropertiesPrintNameFreeze1Sa

5、mpleGenrSheet|Graph5tats|IdentSeries:RHSample11444Observations1443Mean0.000270Median0.000563Maximum0.094008Minimum-0.093342Std.Dew0.016318Skewness-0.145826Kurtosis9.086901Jarque-Bera2232.768Probability0.000000n圖7-3同樣的步驟町得收益率iz的描述性統(tǒng)計(jì)量。觀察這些數(shù)據(jù)，并得出有關(guān)結(jié)論。-Lag叵仃gthQAutomaticsele匚tiom:|AkaikeInfoCriterion二M

6、aximumlags:4廠Userspecified:4Cancel2平穩(wěn)性檢驗(yàn)(1)再次雙擊選取rh序列，選擇View菜單卞的子菜單“UnitRootTest”，出現(xiàn)如卞窗口(圖74IXlUnitRootTestTesttype(AugmentedDickey-FullerTestForunitrootin丐Level1stdifference廣2nddifferenceIncludeintestequation-CInterceptIrendintercept金None對(duì)該序列進(jìn)行ADF單位根檢驗(yàn)，選擇滯后4階，帶截距項(xiàng)而無(wú)趨勢(shì)項(xiàng)，所以采用窗IIJLnJ兇的默認(rèn)選項(xiàng)，結(jié)果如下圖75：BSe

7、ries:RHWorkfile:UNTITLED:UntitledNameFreeze|SampleGenrSheetGraphStatsIdent|AugmentedDickey-FulleiUurtRootTestonRHViewProcObjectfropertiesprintNullHypothesis:RHhasaunitrootExogenous:NoneLagLwngth:3(AutomaticonAIC,maxlag=4)1-StatisticProb*AugmentedDickey-Fullerteststatistic-17.957920.0000Testcriticalv

8、alues:1%level5%level10%level-2.566570-1.941044-1.616551MacKinnon(1996)one-sidedp-values.圖7-5（2）對(duì)rz做單位根檢驗(yàn)后，得結(jié)呆如圖7-6：圖7-6（3）思考：結(jié)果分別說(shuō)明數(shù)據(jù)序列山、rz是穩(wěn)定的還是非穩(wěn)定的？3.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rh和rz的自相關(guān)檢驗(yàn)（在ih序列窗II，點(diǎn)擊viewcorrelogiam）,我們發(fā)現(xiàn)兩市的收益率都與其滯后15階存在顯著的自相關(guān)（思考：如何通過(guò)Eviws檢驗(yàn)），因此對(duì)兩市收益率的均值就其滯后15階做自回歸，方程都采用如卞形式：+c二5+呂（1）

9、對(duì)收益率山做自回歸在Eviews主菜單中選擇“Quick”，并在卜拉菜單中選擇“EstmiationEquationv,出現(xiàn)如下窗口圖7-7IXlEquationEstimationSpeizifiizationOptions-EquationspecificationDependentvariableFollowedbylistofregressorsincludingARMAandPDLtermsORanexplicitequationlike學(xué)之+以51%rhcrh(-15)|EstimationsettingsMethod:|ls-LeastSquares(NLSandARMA)Sam

10、ple:t1444OKCancel圖7-7在“Method”中選擇LS（即普通最小二乘法），然后在“Estimationsettings上方空白處輸入圖示變量，單擊“OKS則出現(xiàn)圖Inl兇StatsResids曰Equation:UNTITLEDWorkfile:UNTITLED:UntitViewProcObjectPrintNameFreezeEstimateForecastDependentVariahl巴RHMethod:LeastSquaresDate:05/16/18Time:22:10Sample(adjusted):171444Includedobservations:1428

11、afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C0.0002260.0004300.5255780.5993RH(-15)0.0939430.0262943.5728590.0004R-squared0.008872Meandependentvar0.000253AdjustedR-squared0.008177S.D.dependentvar0.016331S.E.ofregression0.016264Akaikeinfocriterion-5.398315Sumsquaredresid0.377206Schwarz

12、criterion-5.390942Loglikelihood3856.397HannanQuirincriter.-5.395562F-statistic1276532Durbin-Watsonstat2.022451Prob(F-statistic)0.000365圖7-8（2）用Ljung-BoxQ統(tǒng)計(jì)量對(duì)均值方程擬和后的殘差及殘差平方做自相關(guān)檢驗(yàn)：選擇“View”菜單下“ResidualTest子菜單的項(xiàng)，則可得該方程殘差項(xiàng)的自相關(guān)系數(shù)acf值和pacf值（在LagSpecification窗口中選10階），如圖7-9Date:05/16/18Time:22:15Sample:1144

13、4Includedobservations:1428Qstatisticprobabilitiesadjustedfor1dynamicregressorAutocorrelationPartialCorrelationACPACQ-StatProbi1i11-0.011-00110.186206661112-0.035-0.0351.89140.388iI*1130.0110010207270557i1140.05800576.85640144|1115-0.018-0.01e7.33790.197111160.00700107.40200285111170.0220.0208.111e0.

14、323(1(18-0.037-003910.0730260(119-0.045-0.04313.0250.1611111100.013000813.2560210圖7-9Series:RE5IDO2Workfile:UNTITLED:UntitledViewProcObjectPropertiesPrintNameFreeze|SampleGenrSheetGraph5tats|IdentConeloyrainofRESID02Date:05/16/18Time:22:22Sample:11444Includedobsetvatio1428AutocorrelationPartialCorre

15、lationACPACQ-StatProbii10.1750.17543.6310.000ii320.1400.11371.7680.000iiJ30.1490.113103.650.000ii40.1200.069124.210.000i1ii50.046-0.011127.230.000ii60.0990.063141.430.000iiJ10.0760.031149.670.000i1ii80.0520.012153.550.000i1ii90.0490.012157.000.000iip100.0750.040165.070.000圖7-10(3)在命令欄中輸入命令：gemresl=i

16、esid-2,得到該方程殘差平方的數(shù)據(jù)序列resl（2）同樣，可得序列rz的回歸方程及回歸方程殘差項(xiàng)的acf值和pacf值，如圖7J1和圖7-12:圖7-9到7-10表明兩回歸方程的殘差都不存在顯著的自相關(guān)，但殘差平方有顯著的自相關(guān)。圖7-11圖7-12（5）對(duì)殘差平方做線(xiàn)性圖。雙擊選取序列resl,在新出現(xiàn)的窗I1中選擇“View”菜單下的“LineGraph”，得到resl的線(xiàn)性圖如圖7-13圖7-133同樣的，rz的殘差平方res2的線(xiàn)性圖如圖：圖7-14觀察門(mén)J以發(fā)現(xiàn)波動(dòng)具有明顯的時(shí)間可變性(tmievaiymg)和集簇性(clustermg)(6)對(duì)殘差進(jìn)行ARCH-LMTest依照

17、步驟(1),再對(duì)山做一次滯后15階的回歸，在出現(xiàn)的equation窗II中選擇“View”菜單下“ResidualTest”子菜單的“Arch-LMTestw項(xiàng)(取滯后一階),得如下結(jié)果(圖7-15)：HeteroskedasticityTest:ARCHF-statistic44.81901Prob.F(1J425)0.0000Obs*R-squaied43.51334Prob.Clii-Square(l)0.0000TestEquation:DependentVaiiable:RESID八2Method:LeastSquaresDate:05/16/18Time:22:48Sample(a

18、djusted):181444Includedobservations:1427afteradjustmentsVariableCoefficientStd.Enort-StatisticPiob.C0.0002182.08E-0510.468990.0000RESIDA2(-1)0.1746250.0260846.6947000.0000R-squared0.030493Meandependentvar0.000264AdjustedR-squaied0.029813S.E.ofregression0.000743Sumsquaredresid0.000786S.D.dependentvar

19、AkaikeuifocriteiionSchwarzcriterion0.000754-11.57130-11.56392Loglikelihood8258.120Hamian-Qunmcriter.-11.56854F-statistic44.81901Durbm-Watsonstat2.039487Prob(F-statistic)0.000000圖7-15對(duì)方程回歸后的殘差項(xiàng)同樣可做Arch-LMTest,結(jié)果如圖7-16：|evs-Equation：UHTITLEDlorkfile：UBTITLED-|njx|FileEditObjectsViewProcsQuickOptionsWi

20、nd.owHelp-Ifflx|View|Procs|Objects|Print|Freeze|Estimate(Forecast|Stats|Resids|ARCHTest:F-statistic47.51115Probability0.000000Obs*R-squared46.04172Probability0.000000TestEquation:DependentVariable:RESID幾2Method:LeastSquaresDate:10/27/05Time:17:21Sample(adjusted):181443Includedobservations:1426aftera

21、djustingendpointsVariableCoefficientStd.Errort-StatisticProb.C0.0002622.39E-0510.973760.0000RESIDA2(-1)0.1796890.0260696.8928330.0000R-squared0.032287Meandependentvar0.000319AdjustedR-squared0.031608S.D.dependentvar0.000858S.E.ofregression0.000845Akaikeinfocriterion-11.31410Sumsquaredresid0.001016Sc

22、hwarzcriterion-11.30672Loglikelihood8068.952statistic47.51115Durbin-Watsonstat2.062131Prob(F-statistic)0.000000Path=c：eviews3DB=noiteWF=untitled圖7-16得到的結(jié)果同樣說(shuō)明殘差中ARCH效應(yīng)是很顯著的，因此考慮進(jìn)行GARCH類(lèi)模型建模。GARCH類(lèi)模型建模選擇“Quick”菜單下“EstimateEquation”菜單，在出現(xiàn)的如圖7-17窗II中輸入圖示變量，點(diǎn)擊“OK”鍵后得到山數(shù)據(jù)序列的GRACH(1,1)模型估計(jì)結(jié)果，如圖7-18:。Equat

23、ionSpecificatioil)epenctentfollowedbyregressorsandARMAterms：:rhcrh(15)上J富IMeanEquationSpecification:ARCHSpecification:OrderARCH:lGARH廠F；GARCH(symmetric)TARCH(asymmetric)EGARCHComponentARCHAsymmetricComponentVarianceRegre$sor$:ARCH-Mterm:卷NoneStd.Dev./VarianceCancelMethod:jARCHAutoregressiveCondition

24、alHeteroskedastick|Sample:11443-Enterregre$or$forComponentModelintheorder:permanenttransitoryEstimationSettings:圖7-17EViews-Equation：UHTITLEDVorkfile：UBTITLIDFileEditObjectsViewProcsQuick0tionsWindowHelp|(91X|View|Procs|Objacts|Frin11N：ame|Freeze|Estimate|ForesstStats|Resids|DependentVariable:RHMeth

25、od:ML-ARCHDate:10/27/05Time:17:29Sample(adjusted);171443Includedobservations:1427afteradjustingendpointsConvergenceachievedafter26iterationsCoefficientStd.Errorz-StatisticProb.C-2.21E-060.000283-0.0077950.9938RH(-15)0.0593020.0202002.9357450.0033VarianceEquationC8.72E-061.45E-066.0035430.0000ARCH(1)

26、0.1765140.01524211.580780.0000GARCH(1)0.3073910.01370358.920600.0000R-squared0.007543Meandependentvar0.000253AdjustedR-squared0.004751S.D.dependentvar0.016348S.E.ofregression0.016309Akaikeinfocriterion-5.645039Sumsquaredresid0.378216Schwarzcriterion-5.626597Loglikelihood4032.735F-statistic2.701826Du

27、rbin-Watsonstat2.023588Prob(F-statistic)0.029225Path=c：eviews3DB=noiteWF=untitled圖7-18同理，iz數(shù)據(jù)序列的GRACH(1,1)模型估計(jì)結(jié)果，如圖7-19：DependentVariable:RZMethod:ML-ARCHDate;10/27/D5Time;17:28Sample(adjusted):171443Includedobservations:1427afteradjustingendpointsConvergenceachievedafter28iterationsCoefficientStd.E

28、rrorz-StatisticProb.C-0.0005780.000327-1.7706770.0766RZ(-15)0.0575420.0219142.6257680.0086VarianceEquationCS.32E-061.29E-064.9065620.0000ARCH0.1221630.00978312.486960.0000GARCH(1)0.3636140.00923193.559230.0000R-squared0.005931Meandependentvar-0.000173AdjustedR-squared0.003135S.D.dependentvar0.017933

29、S.E.ofregression0.017905Akaikeinfocriterion-5.476727Sumsquaredresid0.455886Schwarzcriterion-5.458285Loglikelihood3912.645statistic2.120990Durbin-Watsonstat1.914368Prob(F-statistic)0.075965Path=c：eviews3DB=noiteWF=untitled,!x|FileEditObjectsViewProcsQuickOptionsWindowHelp一|占|X|Xiew|Procs|Objects|Frir

30、t11|Freeze|Estimate|Forcest|Stats|Resids|EViews-Equation：UBTITLEDVorkfile：U1TITLID圖7-19可見(jiàn)，滬深股市收益率條件方差方程中ARCH項(xiàng)和GARCH項(xiàng)都是高度顯著的，表明收益率/；序列具有顯著的波動(dòng)集簇性。GARCH-M(M)估計(jì)結(jié)果在EquationSpecification窗口的“ARCH-M”列表中選擇Variance，單擊OK”鍵后，得如下估計(jì)結(jié)果，圖7-20：|evs-Equation：UHTITLEDWorkfile：UBTITLID-|njx|FileEditObjectsViewProcsQuic

31、kOptionsWindowHelp-Ifflx|View|Procs|Objects|Print|Freeze|Estimate(Forecast|Stats|Resids|DependentVariable:RHMethod:ML-ARCHDate;10/27/D5Time;17:31Sample(adjusted):171443Includedobservations:1427afteradjustingendpointsConvergenceachievedafter29iterationsCoefficientStd.Errorz-StatisticProb.GARCH5.93767

32、12.2596952.6276420.0086C-0.0010310.000450-2.2928810.0219RH(-15)0.0514680.0183492.8048810.0050VarianceEquationC1.20E-051.97E-066.1016540.0000ARCH(1)0.2151830.01860511.565640.0000GARCH(1)0.7645770.01759743.449160.0000R-squared0.014653Meandependentvar0.000253AdjustedR-squared0.011186S.D.dependentvar0.0

33、16348S.E.ofregression0.016266Akaikeinfocriterion-5.646948Sumsquaredresid0.375506Schwarzcriterion-5.623818Loglikelihood4034.384F-statistic4.226334Path=c：eviews3DB=noiteWF=untitled圖7-20同理，收益率iz的GARCH-M(IJ)估計(jì)結(jié)果如卞圖圖7-21：|EViews-Equation：IfflTTITLEDlorkfile：UBTITLIDI口x|IJFileEditObjectsViewProcsQuickOpti

34、onsWindowHelp-101x|View|Procs|Objects|Print|Freeze|Estimate(Forecast|Stats|KesidsjDependentVariable:RZMethod:ML-ARCHDate;10/27/D5Time;17:34Sample(adjusted):171443Includedobservations:1427afteradjustingendpointsConvergenceachievedafter33iterationsCoefficientStd.Errorz-StatisticProb.GARCHCRZ(-15)5.162

35、608-0.0015590.0549652.1653440.0005490.0222132.384198-2.8387312.4740050.01710.00450.0134VarianceEquationCARCH(1)GARCH(1)6.85E-060.1232680.3608481.39E-060.0095670.0093754.93939012.8846991.826440.00000.00000.0000R-squaredAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihood0.0085530.0050640.017

36、8880.4546843912.653MeandependentvarS.D.dependentvarAkaikeinfocriterionSchwarzcriterionF-statistic-0.0001730.017933-5.476337-5.4532072.451695Path=c：eviews3DB=noiteWF=untitled圖7-21很明顯，滬深兩市均值方程中條件方差項(xiàng)GARCH的系數(shù)都是顯著的。它們反映兩市的收益與風(fēng)險(xiǎn)的正相關(guān)關(guān)系，也說(shuō)明收益有正的風(fēng)險(xiǎn)溢價(jià)，而且上海股市的風(fēng)險(xiǎn)溢價(jià)要高于深圳I。這說(shuō)明上海股市的投資者更加地厭惡風(fēng)險(xiǎn)(riskaverse),要求更高的風(fēng)險(xiǎn)補(bǔ)償

37、。、對(duì)股市收益波動(dòng)作非對(duì)稱(chēng)性的研究1、TARCH模型估計(jì)結(jié)果與GARCH(1,1)不同的是，在圖7-17中的“ARCHSpecification下拉列表中選擇TARCH”，則得ill、rz的TARCH模型估計(jì)結(jié)果，分別如下圖7-22和圖7-23：|evs-Equation：UHTITLEDWorkfile：UBTITLID-|njx|FileEditObjectsViewProcsQuickOptionsWindowHelp-Ifflx|View|Procs|Objects|Print|Freeze|Estimate(Forecast|Stats|Resids|DependentVariabl

38、e:RHMethod:ML-ARCHDate;10/27/D5Time;17:35Sample(adjusted):171443Includedobservations:1427afteradjustingendpointsConvergenceachievedafter26iterationsCoefficientStd.Errorz-StatisticProb.C-0.0002540.000319-0.7965900.4257RH(-15)0.0653160.0216053.0231770.0025VarianceEquationC7.2SE-061.17E-06S.2074090.000

39、0ARCH0.1106670.0136788.0908990.0000(RESIDARCH0.0852590.0184464.6220440.0000GARCH(1)0.3318560.01107075.147700.0000R-squared0.007253Meandependentvar0.000253AdjustedR-squared0.003760S.D.dependentvar0.016348S.E.ofregression0.016317Akaikeinfocriterion-5.649883Sumsquaredresid0.378326Schwarzcriterion-5.627

40、752Loglikelihood4037.191F-statistic2.076461Path=c：eviews3DB=noiteWF=untitled圖7-22劉！-Equation：IfflTTITLEDlorkfile：UBTITLIDI口x|1JFileEditObjectsViewProcsQuickOptionsWindowHelp-Ifflx|View|Procs|Objects|Print|Freeze|Estimate(Forecast|Stats|Resids|DependentVariable:RZMethod:ML-ARCHDate;10/27/D5Time;17:36

41、Sample(adjusted):171443Includedobservations:1427afteradjustingendpointsConvergenceachievedafter29iterationsCoefficientStd.Errorz-StatisticProb.C-0.0007840.000355-2.2068130.0273RZ(-15)0.0562720.0222442.5297300.0114VarianceEquationC5.74E-0S1.23E-064.6717460.0000ARCH0.0968020.0097519.9273740.0000(RESID

42、ARCH0.0478690.0143873.3272750.0009GARCH(1)0.3678770.00875499.140710.0000R-squared0.005205Meandependentvar-0.000173AdjustedR-squared0.001705S.D.dependentvar0.017933S.E.ofregression0.017918Akaikeinfocriterion-5.478776Sumsquaredresid0.456219Schwarzcriterion5.456645Loglikelihood3915.106F-statistic1.4869

43、95lwnPath=c：eviews3DB=noiteVF=untitled圖7-23估計(jì)結(jié)果中，(RESIDVO)*ARCH(1)的系數(shù)都人于零，而且顯著，并有山方程的系數(shù)大于rz方程的系數(shù)，這說(shuō)明滬深股市中壞消息引起的波動(dòng)比同等人小的好消息引起的波動(dòng)要大，滬深股市都存在杠桿效應(yīng)。EARCH模型估計(jì)結(jié)果在圖7-17的“ARCHSpecificationM下拉列表中再次選擇EGARCH”，則得ill、rz的EGARCH模型估計(jì)結(jié)果，分別如下圖7-24和圖7-25：|evs-Equation：UHTITLEDWorkfile：UBTITLID-|njx|FileEditObjectsViewPr

44、ocsQuickOptionsWindowHelp-Ifflx|View|Procs|Objects|Print|Freeze|Estimate(Forecast|Stats|Resids|DependentVariable:RHMethod:ML-ARCHDate;10/27/D5Time;17:43Sample(adjusted):171443Includedobservations:1427afteradjustingendpointsConvergenceachievedafter57iterationsCoefficientStd.Errorz-StatisticProb.C-0.0

45、002760.000304-0.9080020.3639RH(-15)0.0543250.0209702.5906690.0096VarianceEquationC-0.5127700.057778-8.8748670.0000|RES|/SQRGARCH(10.2798140.02221812.593650.0000RES/SQRGARCH(1)-0.0518460.011154-4.6481150.0000EGARCH(1)0.9639030.005960161.71940.0000R-squared0.006390Meandependentvar0.000253AdjustedR-squ

46、ared0.002893S.D.dependentvar0.016348S.E.ofregression0.016324Akaikeinfocriterion-5.667941Sumsquaredresid0.378655Schwarzcriterion-5.645811Loglikelihood4050.076F-statistic1.827619Path=c：eviews3DB=noiteWF=untitled圖7-24|evs-Equation：UHTITLEDWorkfile：UBTITLID-Inx|FileEditObjectsViewProcsQuickOptionsWindow

47、Help-Ifflx|View|Procs|Objects|Print|Freeze|Estimate(Forecast|Stats|Resids|DependentVariable:RZMethod:ML-ARCHDate;10/27/D5Time;17:41Sample(adjusted):171443Includedobservations:1427afteradjustingendpointsConvergencenotachievedafter100iterationsCoefficientStd.Errorz-StatisticProb.C-0.0007750.000340-2.2

48、776340.0227RZ(-15)0.0494520.0223732.2103260.0271VarianceEquation0-0.3713630.045840-8.1012130.0000|RES|/SQRGARCH(10.2368450.01587114.923010.0000RES/SQRGARCH(1)-0.032059.008898-3.6027780.0003EGARCH(1)0.9764660.005116190.88180.0000R-squared0.004809Meandependentvar-0.000173AdjustedR-squared0.001307S.D.d

49、ependentvar0.017933S.E.ofregression0.017922Akaikeinfocriterion-5.487199Sumsquaredresid0.456401Schwarzcriterion-5.465068Loglikelihood3921.116F-statistic1.373207Path=c：eviews3DB=noiteWF=untitled圖7-25在EGARCH中，RES/SQRGARCH（1）項(xiàng)系數(shù)的估計(jì)值都小于零而且顯著，這也說(shuō)明了滬深股市中都存在杠桿效應(yīng)。（三）、對(duì)滬深股市作波動(dòng)溢出效應(yīng)研究1.檢驗(yàn)兩市波動(dòng)的因果性（1）提取條件方差重復(fù)步驟（7）,選擇主菜單欄Procs下的“MakeGarchVarianceSenes”，得到殘差項(xiàng)的條件方差數(shù)據(jù)序列GARCH01,同樣的步驟得sz回歸方程殘差項(xiàng)的條件方差數(shù)據(jù)序列GARCH02。（2）檢驗(yàn)兩市波動(dòng)的因果性在“Woikfile中同時(shí)選中“garchO1”和“gaich02”，右擊，選擇“Ope