計(jì)量EVIEWS實(shí)驗(yàn)報(bào)告

1、CPI與M1的關(guān)系(1) 導(dǎo)入數(shù)據(jù)，建立工作組(2) 生成cpi數(shù)據(jù)列對(duì)其進(jìn)行ADF檢驗(yàn)，一階平穩(wěn)，結(jié)果如下:t-StatisticProb.*Auqmented Dickey-Fuller test statistic-2.214235D.0270Test critical values:1% level5% level iO%Jevel*2.606911 -1 9467B4 -1.613062同樣方法生成ml數(shù)據(jù)列，并對(duì)其進(jìn)行ADF檢驗(yàn)，一階平穩(wěn)，結(jié)果如下:t-StatisticProb*Augmented Dickey-Fuller test statistic-0.9209930.00

2、00Test critical values:1% level 5% levei 10% level-2.605442-1.9465491.61 3181(3) 以上實(shí)驗(yàn)說明CPI與M1是一階單整序列， 接著對(duì)DM1與DCPI進(jìn)行協(xié)整檢 驗(yàn)，Quick Group StatisticsCointegration Test檢驗(yàn)結(jié)果如下：Date: 04/1Q/12 Time: 15:59Sample (adjusted): 2007M06 2012M02 included observations: 57 after adjustments Trend assumption: Linear de

3、terministic trend Series: DCPI DM1Lags interval (in first differences): 1 to 1Unrestricted Cointegration Rank Test (Trace)Hypothesized NoofCEfs)EigenvalueTraceStatistic005Critical ValueProb *None *0.36132233.2374915494710.0000At most 1 *0.1268407.7313043.84146600054Trace test indicates 2 cointegrati

4、ng eqn(s) at the Q.05 level 卞 denotes rejection of the hypothesis at the 0.05 level *MacKinnon-Haug-Michelis (1G99) p-valuesUnrestricted Cointegration Rank Test (Maximum Eigenvalue)HypothesizedNo. ofCECs)EigenvalueMax-Eigen Statistic005Critical ValueProb.*None*0.36132225.65619U.2S4600.0006Atirrost 1

5、 *01268407 7313043.8414S60.0054Majteigenvalue test indicates 2 coin怕grating eqn(s) at the 0.05 level * denotes rejection nfthe hypothesis at the 0 05 level *MacKinnon-Haug-Michelis (1999) p-values說明存在2個(gè)協(xié)整關(guān)系。Group(4 )對(duì)其一階差分序列進(jìn)行格蘭杰因果檢驗(yàn)，QuickStatisticsGran ger Causality Test檢驗(yàn)結(jié)果如下：Pairwise Granger Caus

6、alilyTestsDate: 04/10/12 Time: 15:51Sample： 2007M03 2012IW02Lags: 2Null Hypothesis:ObsF-StatisticProb.DM1 does not Granger Cause DCPI573.676100.0321DCPJ does not Granger Cause DM10717440.4026說明M1是CPI變動(dòng)的格蘭杰原因，而 CPI不是M1變動(dòng)的格蘭杰原因 綜上所述，CPI與M1存在協(xié)整關(guān)系，且M1的變動(dòng)引起CPI的變動(dòng)。滬深股市收益率波動(dòng)性分析一滬深股市收益率的波動(dòng)性研究1、描述性統(tǒng)計(jì)Series：

7、RSHANGSampie t 1Q33Observation j 1032Mean-D.ODO824Lied ian0.000542tilaximum0.090342MinimuiTi-0.CSO437Std. Dev.0.019323Skewness4 146983Kurtosis57&768331B9724Probability0.000000圖一上證收益率Senes: RSHEFJSample 1 1031Otserva：inns 1032Mean-0 000620Median0.000241Maximum0.Q9161SMin mum-0.GS6103SM. Uev.0.D21

8、6Q1Skewness-0.253026Kurtoai4 638S2Sarqufr-Bera156 4067Probability0.000000圖二深證收益率從圖一可以發(fā)現(xiàn)，樣本期內(nèi)滬市收益率均值為-0.000824 ,標(biāo)準(zhǔn)差為 0.019323，偏度為-0.146983，峰度為5.707683，遠(yuǎn)高于正態(tài)分布的峰度值 3, 說明收益率rt具有尖峰和厚尾特征。JB正態(tài)性檢驗(yàn)也證實(shí)了這點(diǎn)，統(tǒng)計(jì)量為 318.9724,說明在極小置信概率下，收益率顯著異于正態(tài)分布；從圖二可以發(fā)現(xiàn)， 樣本期內(nèi)深市收益率均值為-0.00062,標(biāo)準(zhǔn)差為0.021601，偏度為-0.253026,峰 度為4.83882

9、5,收益率同樣具有尖峰厚尾特征。深市收益率的標(biāo)準(zhǔn)差大于滬市， 說明深圳股市的波動(dòng)更大。2、平穩(wěn)性檢驗(yàn)變量t-統(tǒng)計(jì)量伴隨概率穩(wěn)定性上證收益率-32.231290.0000平穩(wěn)深證收益率-30.609250.0000平穩(wěn)在5%的置信水平下，上證收益率與深證收益率都是平穩(wěn)序列，可以進(jìn)行回歸分3、均值方程的確定及殘差序列自相關(guān)檢驗(yàn)EVie窯-Series: KSHANGTorkfile:：1J3 file Edit Qbject 底iew FrQuick Oilioits Window Melp 單已w ptoclobjett Propytim PrintMam亡Freeze bamtpl亡麗訶囹上治

10、代旳沁店Date: 04/05/12 Time: 21:07Sample: 1 1033included observations: 1032AutocorrelationPartial CorrelationACFACQ-StatProbi11100050.0050.02470.S75'1II2-0011-0.0110 149B0.928130.0240.0240.76920.85711140 0420.042256460.63311115-0.006-0.0052.59610.762116-0.035-0.0353.90490.6901700230 021447940.723111

11、Q-0013-0.015465050.79411IIg-0.022-0.0195.13600.82211II1000040.0055 15340.S81111110001-0.0015.15370.923111II120.0220.0235.65560.93211130.0590.Q639.356707461114-0.044-0.04511.346Q.6591150.04&0.047137240.547'1II16-0017-0.0221403B0.596II17-0026-0.02914.74B0.6141180 0460.05017.0100.522IIIII190014

12、0.01417.2170.5751120-0.033-0.03418.3480.565圖三 上證收益率自相關(guān)檢驗(yàn)Date: 04；05/12 Time: 21:16Sample： 11033Included observations: 1032AutocorrelationPartial CorrelationACPACQ-StatProb1i100470 047225760 133(112-0040-0 0423.89200 1431I-1130 01100154.026902591斗003700345 4S740243H115*0.018*0.0215 32240.324H|15-0.02

13、4*0.020&43980.37611110.0380.0387.98220.334|(13-0.023-0.0308.53450.38311|90017-0010B.&4010452II11110001900199.221705111111110 01100059.3563053911100490,05311 8210460111300430.040137250.39311>U-0.033*0.03914.8310.3S6II11150.0270.03515.5310.407(116-0.025-0.03416.29904321(1170036-003417691040

14、9111180 0380 04619.200038011車1!?0.039002820.8100.347(11120-0042-0 04322 7130303圖四 深證收益率自相關(guān)檢驗(yàn)從圖三和圖四可以看出上證收益率和深證收益率都不存在自相關(guān)性，因此我們選擇以下模型作為波動(dòng)率模型的均值方程：it =Uit ；it i =1,2其中口 t表示上證收益率，J t表示深證收益率。對(duì)滬深市場(chǎng)收益率分別作如上模型的回歸，結(jié)果如圖五和圖六所示：Equat ion: UBTITLEDTorkfHe: 1: : FIkJECFreeze Estimate ForecastRes idsProcDep enden

15、t /ariable: RSHANGMethod: Least SquaresDate: 04/05/12 Time 21:12Sample (adjusted): 2 1033In eluded observations: 1032 after adjustmentsCoefficientStd Error卜StatisticProbc*0.0008240 000601-1.3704140.1709R-squared Adjusted R-squared S E of regression Sum squared residLog likelihood Durbin-Watson stat0

16、.0000000.000000 0 019323 03B49472608.9092.009039Mean dependent varS.D. dependentvar Aka ike info criterionSchwarz criterion Hannan-Quinn crite匚-Q.000824 0 019323-5.0540S7-5.049301-5.052271回區(qū)圖五上證收益率回歸分析| l'i亡 Win t 晶|尸皀亡已 底tiirote |向弐邛 t®tats 吸凹 ds Dependent Variable- RSHEN Method: Least Squ

17、ares Date: 04/05/12 Time: 21:19 Sample iadjusted.: 2 1033 Included observations: 1032 after adjustmentsCoefficientStd Errort*StatisticProb,C-0.0006200.000672-0.921S750.3568R-squared0000000Mean dependent 0 000620-.djusted R-squared0.000000S.D. dependent var0.021601S.E. of regression0,021601ike info c

18、riterion-4.831222Sum squared resid0 481050Schwarz aiteri an-4820436Log likelihood2493 911Hannan-Quinn crite r.-4.829406Durbin-Watson stat1.906299圖六深證收益率回歸分析4、用Ljung-Box Q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)EVieTS fEqiaat i nzix nHTTTI-EDi n;rkf i 1 吐：I * - j| 訐 EViewsEquati 口:p: ITHTTTI.EDforkfil|l1* EdJ t gbj

19、p" Yiftf Frac 卜,ck OptionsFil* Eli IPf&c g規(guī)ck Otkofis Vindv M«lpfew斎 prW)(HMlhr6eai危HfflateFor«ari§ 情低Tim?- 21：i3Sample3 2 1Q33Included observations: 1032Date: CkUOS/13 Time- 2l:H4Sample 2 1013Included Dbserodians' 1032AutocorTelaUonPartiall CorrelationACPACd抽Probi1!|i1-O

20、.OOfi-0.M5D.0247O.B75i1i|2-0.011-0.0110.149S0.928ii3D.0240.0240.76920.B57ii14M20.04225M4fli.639iIi15-4).0064.0052.59010.76211§MJ.Q95-0.0353.90490.690ii170.0230.0214.47940.723il*II0-0.013-00154.65050794i1II9-0 0225.1350Q622i1i1W0.DQ40OT55.1534i1i1110 001-0 W15.1537。睨3ii1ISDO22a«36.65H0財(cái)i1i1

21、30.059。帖39.3557074611H14-4.044£.04 弓11.34&oiBsai31115ALCwa13.7240.547!iI1Ifi-0.(12214.Q»iIl>17-0.026-0.02914.74S0J14Ii1iaD.0460.G5017.0100.5221i1竹D.0140.01417.2170.575i120-D.093-0.G3418.3480.565jjEccorrelatiDnF'arljall Carrel alienACRACO-Sta1Probii10.1520.15224.0030.000i1i!20.06

22、30.0412B.142O.DOOiiZl30.1400.1274艮剜0.000ii斗0.1490.11371.307O.QQOii50.111Q.O&9B4J&7ODDOii1&<M41Q碩104.70ODDOii7Q160 1171M15ODDOiiQ135QJ&70153.14ODDOi3i190PB5g ®i71W.S1ODPOi口iJ10a.175.113192.7«"DDiIi110.DS4-0.009.200.25ODDOii120.D3B-0.&12201.G9-O.DODii1130.1320.M32

23、19.390-000iQii140.0990.M7230.10&D00IJii150.0680.M3234.970,000i3i1啊0.0840.017242.39O.DDOiJi170.0890.012250.60(kDOOii1180.0870.024258.01O.DQOii190.0720.&1B264.09ooooii120d1210056279.55»DDO圖七上證收益率回歸模型 殘差的自相關(guān)檢驗(yàn)圖八上證收益率回歸模型殘差平方的自相關(guān)檢驗(yàn)ETiers - 任朝吐込陽 DVTITLED Vorkfilf I;: 1EViews Equati nni 1IHT

24、 i TIED Vnrk-f i 11 x JFlI* Edit QbjVitw Froic 爭(zhēng)ii此k OgtinsHftlpDale 0-wa5fi2 Time: 2iD sample: 2 祁33 induded obs«rallon$: 1032.djLocofrelalion Partial Cwelalion AC FAC QSlart ProbDate:Time 21:51Sample: 2 TO加Included! obBeratians 1032AuEDcarrBlabDn Partial Comlarli口nAC PA Q-Slat Prob10.0470.047

25、2.2§76o.ns2-0.0400.0423.8200.14330.011D.0154.0価0 259丄Q.0370,0345.4«740 243-0.018-0.0S15 82240 35460.024-0.020G439a0環(huán)7D.Q390.0397.93220 3J48«0.023-0.0309.53450.3330-0.017-0.0109.9010.452100.0190.0199.22170.511110.0110.0059.3S30.5&9120.0490.0S311.S210.4&0130.0430.04011T2S0.39314

26、-0.033-0.03914.3S10珈1-Q.0270.03515S310 407護(hù)-0.026-0.034162W0 432廣-0.036=0.03417.5910 4918D.Q3819.2TO0 350190.0390.02820.SW0.34720-0.042-0.04322.713II110.t020.10210.8D60.00120.098-0.06720.4160.00030.11&0.09332.S&B0.0004D.1W0107507D.OOG5D.09S0 05057 Ml0.0005D 1?i0 091-=：=0.0007Q.157Q11I5咖鶴gtn時(shí)

27、012713227D.DQGg0.052135 OS0.000ID0.1630.107162.50D.OOG11D.04T-*.D2S165.230.00&120.054-O.0U168.28D.000'130.1540.100192.960.000'140.091D.D09201.640.000150.051-0.01820060.000160.12®0.053219 5B0.000170.074O.DD-1225.370.0001B0.054015229 4Q0.00019D.Q&222 «o.ooa2*0QQ9?0 00924Q 59Q

28、Q09圖九 深證收益率回歸模型 殘差的自相關(guān)檢驗(yàn)圖十深證收益率回歸模型殘差 平方的自相關(guān)檢驗(yàn)L_k filefibjtci JLtw £r&c 血Luk Ostans I：ridaw |jtlp、心姑寂比用出pmtlRamejFfEEze 0U伯吋円膽£3創(chuàng)麗包艮eadj從圖七和圖八可以發(fā)現(xiàn)，上證收益率回歸模型的殘差不存在自相關(guān)性，而上 證收益率回歸模型的殘差平方存在很強(qiáng)的自相關(guān)，即模型殘差存在條件異方差。 從圖九和圖十可以發(fā)現(xiàn)，深證收益率回歸模型的殘差不存在自相關(guān)性， 而深證收 益率回歸模型的殘差平方存在很強(qiáng)的自相關(guān)，即模型殘差存在條件異方差。5、ARCH-LM

29、檢驗(yàn)Equation: UNTITLEDTorkflie; 1:;1k卜'環(huán)護(hù)低旳亡電print (Name Free比Estims【已 |F(pr已Heteroskedasticity Test ARCHF-Statistic24.43469 Prob F(1J029)0.0000耳Obs*R-squared23.91431Prob ClibSquare(l)0.0000圖一上證收益率回歸殘差的ARCH檢驗(yàn) Equation: UVTITLED Torkfile: 1: 1回區(qū)jvieA Proc(object| PrintName) Freeze Estimate (Forecas

30、t (stats ResidsHeteroskedasticit/ Test ARCHF-statistic10.86074Prob F1 1029)0.0010ObsfrR*squareil1070819Prob Chi-Square(l)0.0010圖十二上證收益率回歸殘差的ARCH檢驗(yàn)從圖十一和圖十二可以看出上證收益率與深證收益率的殘差都具有ARCH效應(yīng)，因此有必要進(jìn)行GARCH建模來改善模型。6、GARCH類模型建模(1) GARCH(1，1)建模EVie呂-Equation.: UNTITLED Torkfile:File Edit Dbj«ct ¥i«

31、w Froc Quick Otioni Vindw H«lp¥泄虬'卩廣皿0罰亡廿PrinthJmmTFe亡z已|Eshm占匚For亡cj£t矗tats収esi£Dependentvariable. RS HANGMethod: ML -ARCH J.1arquardt) - Normal distributionDate: 04/05/12 Time: 21:52Sample (adjusted); 2 1033Included observations: 1032 alter adjustmentsConvergence achieved a

32、fter 11 iterationsPresample variance: backcast: parameter = 0.7)GARCH = G(2) + COfRESlDCdrZ + C4)*GARCH(-1)CoefficientStd Errorz-StatisticProb.c-0 0004010.000492-0.8161190.4144Variance EquatioriC4.44E-061.21E-0S3.6611000.0003RESID i：-1：ft20 0660120.0080208.2306470.0000GARCH()0.9223180.008833103.&

33、;S470.0000R-squared-0.000480Mean dependent var-0 000824Mu sled R-squared-0.003399S.D. dependent ;ar0.019323S.E. of regression0.019356Akaike info criterion-5.254344Sum squared resid0.385132Schwarz criterion-5235200Log likelihood2715 242Hannan-Quinn alter.-5.247079Durbin-Batson stat2,008076圖十三 上證收益率GA

34、RCH(1,1)模型E¥ie>s - Equation: UNTITLED Torkfile: 1:1File Edit Objact View Froc Quick Op.tions Window Help "ie訓(xùn)PoddbjHt Print Name Fze Estimat亡ForecastbtatsResiclsDependent Variable: RSHENMethod: ML-ARCH (Marquardt - Normal distributio nate: 04705/12 Time: 21:53Sample (adjusted): 21033Inc

35、luded observations: lOM衛(wèi) after adjustmentsConvergence achieved after 6 iterationsPresample variance: backcast parameter = 0.7;GARCH 二 C2J + C(3f RESID(-12 + C4)*GARCH(-1;CoefficientStd. Errorz?StatisticProb.C-0.0003800.000570-0.6663700.5052Varianum EquationC6.10E-061.S4E-0633207050.0009RESID (-120.0

36、557670.0004036.5690000.0000GARCH(-1)0.9311250.00999393.132700.0000R-squared-0.000123hlemn dependent陽-0.000620Adjusted R-squared-0.003042S.D. dependent var0.021601S.E. of regression0.021633Akaike info criterion-4.966025Sum squared resid0.481110Schwarz criterion-4.946800Log likelihood2566.469Hannan-Qu

37、inn criter.-4.953760Durbin-Watson stat1.906063圖十四 深證收益率GARCH(1,1)模型從圖十三和十四可見，滬深股市收益率條件方差方程中ARCH項(xiàng)和GARC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。滬市中ARCH項(xiàng)和GARCH項(xiàng)系數(shù)之和為0.988，深市也為0.986，均小于1。因此GARCH(1,1)過程是平穩(wěn)的，其條件方差表現(xiàn)出均 值回復(fù)(MEAN-REVERSION )，即過去的波動(dòng)對(duì)未來的影響是逐漸衰減。(2)GARCH-M (1,1)E¥ie<s - Eqnatiim: UHTITLED lorkfile: 1

38、: :1口 File Edit Object Vi ewFr oc Qyd ck Ot i ans Vi iLdowHelp¥&； nProcjQbject Printl'imm亡Freeze MbmateF(jeGascstBtsResidsDependent Variable: RSH.ANGMethod: ML- ARCH Marquardt; - Normal distributionDate: 04/05/12 Time: 23:08Sample (adjusted: 2 1033Induded absen/ations: 11032 after adjus

39、tmentsCon;iergence achie'ed after 19 iterationsPresample varianu£： backcast (parameter = 0.7GARCH = C + fRESIDf-l + G(5)*GARCH-1)CoefficientStd. Errorz-StatisticF'rob.GARCH0.0706892.6300420.0267960.97S6C-0.0004330.000363-0.5021240.6156Variance EquationC4.43E-061.24E-063.6241700.0003RESI

40、D (-120.0653330.0080128.2173440.0000GARCH i-1)0.9225050.00393?10322240.0000R-squared-0.000517Mean dependentvar-0.000824Adjusted R-squared-0.004414S.D. dependent var0.019323S.E. nf regression0.019365Akaike info criterion-5.252412Sum squared resid03&5147Schwarz criterion-5.228401Log likelihood2715

41、.244Hannan-Quinn after.-5.243330Durbin-Watson stat2.007939圖十五上證收益率GARCH-M(1,1)模型制 EY讓" Equation: OTITLEDlorkflie: 1: J File E di t Qb j t c t View Proc Quick Oti ons Window Help¥刑ProcOhjMtjEstmat亡戢陌晁険的禹Dependent Variable: RSHENMettiod: ML -ARCH (Marquardt) - Normal distributionDate：04/O5；1

42、2 Time'2110Sample - adjusted: 2 1033Included obser.mtiorih 1Q32 after adjustmentsConvergence achieved after 13 IterationsPresample varianum: backcast (parameter = 0.7GARCH = C(3)* C(4fRESID(-1f2+ C(5rGARCH(-1)CoefficientStd. Errorz-StalisticProb,GARCH237927927799700.8&58650.3921C0.0012910.00

43、1150-1.1225560.2616variance EquationC5.S2EM»1B5E-063.1935080.0014RESlD(-ir20.0556890.0086036.4732360.0000GARCH卜 1)0931840001012692.02356o.ooocsquared-0 001533Mean dependent var-0 000520Adjusted R-squared-0.005434S.D. dependent var0.031601S E of regression0.021659Akaike info criterion-4.964751Su

44、m squared resid0.461798Schwarz criterion-4.940921Log likelihood2566,812Hannan-Quinn enter.-4.955670DurDln<vatsonstat1.900552圖十六深證收益率GARCH-M(1,1)模型從圖十五和十六可見，滬深兩市均值方程中條件方差項(xiàng)GARCH的系數(shù)估計(jì)分別為0.070689和2.379279，這反映了收益與風(fēng)險(xiǎn)的正相關(guān)關(guān)系，說明收益有正的風(fēng)險(xiǎn)溢價(jià)。但是EVn pts - Equation: UNTITLED ¥orkf ile: 1 : : 1兩者都沒有在5%的置信水平下

45、顯著，這可能是因?yàn)橹袊?guó)股票市場(chǎng)沒有很完善，投機(jī)成份大,導(dǎo)致高收益未必高風(fēng)險(xiǎn)。（二）股市收益波動(dòng)非對(duì)稱性的研究1、 TGARCHDependent Variable' RSHANGMethod: ML - .iRCH (Marquardt)-NormaI distributionDate. 04/D5/12 Time： 23:14Sample actjusled? 21033Included observations: 1032 after adjustmentsCofivergencs aGhi&'；Qd 日Hmr 14 iDeTsUonsPresample varia

46、nee: backcast (parameter = 0.7)GARCH = C + C*RESID&i )*2 * C'RESI -1' (RESI (-1 )<0) +C(5)*GARCHP1)CoefTi cientStd. ErrorZ'StStlStlCProb.c-0.0005200.000490-1.2461010.2127.ariance EquationC5.61E-061 33E-064 2035340.0 000RESID(-1 尸20.0333530.0112962.9524790.0032RESlD(-ir2*(RESID(-1K

47、O)0,0522060O12B324.0634810.0 DOOGARCHf-1)0.92297?0.01024590.091230.0 000R-squared-0.000111Mean dependent ；ar-0.000824Adjusted R-squared-0.0&4007S.D. dependent var0.019323E.E. crregresidn0,019362Aksike inro criterion-5261462Sum squared resid0 3&4990Schwarz criterion-5.237532Log lire I i hood2

48、719.915Hannan-Qu inn cmer.-5252381Durbirh-W-atscn stat2 DQ8515圖十七 上證收益率TGARCH模型EVn pts - Equation: UNTITLED ¥orkf ile: 1 : : 1I I File Edjt Objact View Proe Quick ions Vindow HelpDependent .ariable. RSHENMethod: ML - ARCH Marquardt) - Normal distributionDak' 04/05/12 Time'23:14Sample (a

49、djusted)： 21033Included observations: 1032 after adjustmentsConvergence achieved after 13 iterationsFresampie vrianup backcasi (par3meier= 07)G.ARCH = C2) + CfRESIDHSRESIDt-IO) +C(SrGARCHM)CoefficientStd. Errorz-StatisticPratC00006450.000591-1.0910120.2753/arianee EquationC8.06E-D6210&003838818D0001RESID(-1f20.0251070.011&012.1275930.0334RESID (-1 皆(RE SID(-1 )<0)0.0519060.0144713.S867890.0003占ARCH的）0 9296460.0116BG79.639330.0000R-squared-0.000001Mean dependentvar-0000620Adiustsd R-squared0003896S.D. dependent war0.021601S.E. of