(完整版)期貨套期保值比率績效評估

期貨套期保值比率績效的評估金融工程一班2012312570014毛鈺婷一、實驗?zāi)康睦煤唵位貧w模型（OLS）模型、誤差修正模型（ECM）模型和ECM-BGARCH模型估計中國期貨交易所交易的期貨合約的最優(yōu)套期保值比率并對保值效果進行績效評估，說明期貨套期保值在經(jīng)濟生活中的重要作用，并找出績效評估最佳的套期保值比率模型。二、實驗內(nèi)容在實驗過程中使用時間序列分析的方法對整理后的價格時間序列按照上面的理論基礎(chǔ)模型進行建立模型以得到最優(yōu)套期保值比率系數(shù)，其中涉及時間序列分析中的方法有：模型參數(shù)估計，參數(shù)的顯著性檢驗，變量平穩(wěn)性檢驗（含單位根檢驗），回歸殘差項的ARCH效應(yīng)檢驗等，這些過程都將在EVIEWS軟件中進行。三、實驗步驟（一）數(shù)據(jù)的搜集由于期貨合約在交割前兩個月最活躍，使得其價格信息釋放較為充分，更能反映期貨合約的真實價值，所以中國企業(yè)多用距離交割月份較近的期貨合約進行保值，因此我們選擇了在任何一個時點的后一個月進入交割月的期貨合約的中間價格作為分析對象。所以每次取期貨合約時都只用它到期前倒數(shù)第二個月的數(shù)據(jù)，現(xiàn)貨數(shù)據(jù)與期貨數(shù)據(jù)按時間對應(yīng)。若哪一天現(xiàn)貨或期貨有其中一數(shù)據(jù)缺失，則去掉該數(shù)據(jù)以達(dá)到一一對應(yīng)。本實驗從上海金屬網(wǎng)上把AL的11年4月18號到13年4月18號的現(xiàn)貨數(shù)據(jù)截取下來，按上段的方法在同花順平臺上得到相應(yīng)的期貨數(shù)據(jù)并在EXCEL中進行整理，整理后我們得到含有488對期貨（f）、現(xiàn)貨（s）數(shù)據(jù)的EXCEL文件，并命名為FS.由于數(shù)據(jù)量較多，具體數(shù)據(jù)見附錄1。二）用OLS模型估計最優(yōu)套期保值比率先調(diào)整樣本期以便建立F和S的差分序列，再建立F和S的差分序列的回歸方程。Dependentvariable:ISLlethcd:LeastSquaresDate:12-'17/14Time:09:34Sample:24-88Includedobsen/atiians:487CoefficientStd.Errort-StatisticProb.C-3.4505227.978777-0.43&2230.6629IF0.93162?0.012046??.340300.0000R-squared0.924999Ll&andependentrar-42.37166AdjustedR-squared0924345S.D.dependentvar540.9976S.E.ofretiressian175.7262Akaikeinfocriterion13.17983Sumsquaredresid14976556Schwarzcriterion13.19703Loglikelihood-32072E9Hannan-Qjinncriter.13.18359F-statistic5981,'jOODurbin-Batsonstat2775037Prob(F-statistic)0.000000結(jié)果顯示該方程整體上顯著的且解釋變量系數(shù)很顯著（p值為0）,故基本認(rèn)可該回歸模型?；貧w結(jié)果表明每一單位的現(xiàn)貨頭寸要用0.931627位相反的期貨頭寸進行對沖，即最優(yōu)套期保值比為0.931627。（三）用ECM模型估計最優(yōu)套期保值比率1、期貨價格序列即f序列的平穩(wěn)性檢驗Date：Date：-12M7/14Time:09:43Sample:24-88Includedobservations:487Date：Date：-12M7/14Time:09:43Sample:24-88Includedobservations:487ACPAGQ-StatPrab10.9850.985475410.00020.969-0.028936810.00030.953-0.0471383.30.00040.936-0.0051815.00.00050.9190.004223.2.50.000'30.904a.0252625.90.000了0.889a.ooi302970.00080.873-a.0363407.30.000g0.857-a.oi33772.80.000100.8420.0284123.40.000110.827-a.oos4468.20.000120.812-a.oo74798.40.0001307930.038511B..60.000140.784-0.0225425.40.000150.7^0-a.032572750.000160.7560.0026016.20.000V0.745a.1046297.10.000180.734a.0226571.00.000190.725a.oog683860.000200.7Ha.0317100.80.000210709-a.oi87357.50.000220700-a.oo77608.40.000230.6920.0377854.30.000240.687a.0618095.80.000250.68^a.oia9325.30.000260.674-0.0838571.10.000蘆0.665-0.054-8SOD30.000280.'357a.03a9024.50.000290.643-0.029924270.000300.'538-0.0359454.50.000310.027-a.oii9660.10.00032O.'j-IS0.0-139059.90.000甞WW0.6080.01010054.0.000340.597-0.05910242.0.000350.586-a.oi310423.0.0003B0.573-a.03110596.0.000從序列的自相關(guān)系數(shù)沒有很快的趨近與0，說明原序列是非平穩(wěn)的序列。下面對其進行進一步的單位根檢驗。由于資產(chǎn)價格序列往往具有一定的趨勢和截距，因此在includeintestequation中我們選擇同時具有趨勢項和常數(shù)項，即Trendandintercept,得：□ate:12M7/14Time:□ate:12M7/14Time:10:40Sample:2488Ineludedobservations;斗8?NullHypotliesis:FliasaunitrootExogenous:Constant.LinearTrendLagLength:0(AutomaticbasedonSIC,MAXLAG=171t-StatisticProti*AugmentedDichey-Fullerteststatistic-2.1320S90.5261Testcriticalvalues:1%level5%level10%level-3.977013-3.41907G-3.13^097*f.1acKinnon(1995;one-sidedp-values從結(jié)果可以看出ADF檢驗值大于各顯著水平臨界值，且犯第一類錯誤的概率大于0.1，說明我們不能拒絕原序列存在一個單位根的假設(shè)。接著我們對其一階差分序列進行檢驗：NullHypothesis:D(F}liasaunitrootExogenous;Constant.LinearTrendLagLength;0(AutomaticbasedonSIC.MAXLMG=17)t-StatisticProb*AngmentedDicl■:ey-FullerteststatiStic-20.094920.0000Testcriticalvalues:1%level5%level10%level-3.977052-3419095-3.132108*l,1acKinnoni199'jjone-sidedp-values從結(jié)果中可以看出ADF統(tǒng)計量小于臨界值，犯第一類錯誤概率接近為0，說明一階差分序列不存在單位根。綜上兩次檢驗我們可以肯定期貨序列f是一階單整的。2、現(xiàn)貨價格序列即s序列的平穩(wěn)性檢驗ACPACa-statProh10.9850.985475720.00020.971-0.006938.380.00030.955-0.05G1380.80.00040.939-0.00G18.21.20.00050.923-0.0102241.80.000j0.9070.02426-49.40.00070.592-0.00130-44.60.0008O.S7^-0.026S427.00.000g0.S61-0.0253796.50.000in05470.0414154.50.000110.832-0.0264500.50.00012□.SV-0.0014835.20.000130.8040.0495159.80.000U□791-0.011&474.50.00015□777-0.0305779.10.0001'j07640.01060'74.00.0001?0.7530.0766361.10.000190.7440.05566-41.90.000ig0.^350.0066916.90.00020Cl.吃了0.013^186.40.000210719-0.03074&0.30.00022□710-0.001770-8.40.00023□7030.04179'5170.00024-0.'5970.0468211.20.00025□.'5910.0188457.50.0002S□.'584-0.0818099.10.0002?0.675-0.0418935.30.000280.-3'380.019&166.50.000290.658-0.0289391.80.000200.649-0.0219611.00.00031□.'339-0.01398.24.00.000320.'j300.01910032.0.00033□.■320-0.01310233.0.00034□.'509-0.04110428.0.000350.598-0.00510'3U.0.0003S0.586-0.02410'798.0.000從序列的自相關(guān)系數(shù)沒有很快的趨近與0，說明原序列是非平穩(wěn)的序列。下面對其進行進一步的單位根檢驗。由于資產(chǎn)價格序列往往具有一定的趨勢和截距，因此在includeintestequation中我們選擇同時具有趨勢項和常數(shù)項，即Trendandintercept,得：NullH/pothesis:ShasaunitrootExogenous:Constant.LinearTrendLagLength:0CAutamaticbasedonSIC,MAXLW3=17)t-StatisticProb*Augmented□iclrey-Fu11erteststatiEtic-2.1026090.5426Testcritical-/slues:11%le?el-3.9770135^le?el-3.41907610%le-；el-3.19^097TladKinnon(199B)one-sidedp-values.從結(jié)果可以看出ADF檢驗值大于各顯著水平臨界值，且犯第一類錯誤的概率大于0.1，說明我們不能拒絕原序列存在一個單位根的假設(shè)。接著我們對其一階差分序列進行檢驗：NullHypothesis:D[8；hasaunitrootExogenous:Constant.LinearTrEndLagLength:□rAutamaticbasedanSIC.M.AXLAG=17)t-StatisticProb*AuqinentedDickey-Fullerteststatistic-20.3358'j0.0000Testcriticalvalues;1Wlevel5%level10%level-3.977052-3.419095-3.132106*fu!acKinnon(1996}one-sidedp-/alues從結(jié)果中可以看出ADF統(tǒng)計量小于臨界值，犯第一類錯誤概率接近為0，說明一階差分序列不存在單位根。綜上兩次檢驗我們可以肯定現(xiàn)貨價格序列s也不平穩(wěn)，它與期貨價格一樣也是一階單整的。3、對現(xiàn)貨價格序列s和期貨價格序列f的協(xié)整檢驗由于期貨價格序列與現(xiàn)貨價格序列是同階單整的，故滿足協(xié)整檢驗前提。接下來我們用現(xiàn)價格對期貨價格做回歸，用其殘差來檢驗期貨價格序列與現(xiàn)貨價格序列是否存在協(xié)整關(guān)系?，F(xiàn)貨價格對期貨價格的回歸結(jié)果：DependentVariatilGDependentVariatilG：SLIethod:LeastSquaresDate:12/1^>14Time:09:49Sample:14-88Ineludedobsen/ations:488DependentVariatilGDependentVariatilG：SLIethod:LeastSquaresDate:12/1^>14Time:09:49Sample:14-88Ineludedobsen/ations:488CoefficientStd.Errcrt-StatisticProb.C-11G.9671127.4424-0.9178040.2592F1.00^^020.0021274^1.2350a.ooaoR-squared0.997817LIeandependentvar59717.00AdjListedR-squared0.997813S.D.dependentvar5309.294S.E.ofrecsresEion248.3116Akaikeinfocriterion1387134Sumsquaredresid29965105Schwarzc：「itE「iciri13.888^1Loglikelihood-3352606Hannan-Quinncriter.13.87808F-statiStic2221557Durbin-Watsonstat0.535657Prjb(F-slatistic;0.000000再對殘差e序列進彳丁單位根檢驗:NullHypothesis:EhasaunitrootExogenous:HoneLagLeratfh:1CAutomaticbasedanSIC,L1.^XLAG=1?)t-StatiSticProb.*AuginentedDictuy-Fu11e「tostatiStic-5.986475o.aaooTestcriticalvalues:1%le-；Gl51%level-2569729-1.941476-1.61626:2^MacKinnoni：19Q'j；one-sidedp-values結(jié)果顯示在5%的置信區(qū)間內(nèi)可以接受殘差序列e不含單位根的假設(shè)。這說明兩序列協(xié)整關(guān)系存在，因此這里的殘差項e可以當(dāng)作誤差修正項用作建立誤差修正模型。4、建立含有誤差修正項的AF和AS間的誤差修正模型Dependent'Variabl已：Dependent'Variabl已：ISMethod:LeastSquaresDdte:12/17/14Time:09:52Sample:24S3Includedobservations:487Dependent'Variabl已：Dependent'Variabl已：ISMethod:LeastSquaresDdte:12/17/14Time:09:52Sample:24S3Includedobservations:487CoefficientStd.Errort-StatisticProb.c-3.'：gg7317330214-0.5047220.S140IF0.9243490.01108983.398950.0000E[-1；-0.2813880.029559-9.^19606o.aoooR-squared0.936827Meandepend已門tvar-4^37166AdjustedR-s口11日「已id0936566S.D.dependentvar■3+0.9975S.E.ofregression161.4418Akaikeinfocriterion13.01231Sumsquaredresid126-147-11Schwarzcriterion13.03S11L'jglikelihood-3165.437Hannan-Quinn匚「itE「，13.02244F-statistic2598780Durbin--.Vatsonstat2438428ProbrF-statistic;0.000000從F統(tǒng)計量看出該方程整體上是系數(shù)顯著的，自變量系數(shù)和誤差修正項系數(shù)的t統(tǒng)計量都很顯著，故該回歸模型擬合的較好?；貧w結(jié)果表明每一單位的現(xiàn)貨頭寸要用0.924849單位相反的期貨頭寸進行對沖，即最優(yōu)套期保值比為0.924849。四）用ECM-BGARCH模型估計最優(yōu)套期保值比率1、ARCH效應(yīng)檢驗Heteroskedasticit/Test:ARCHF-siatistic1591259Prob.F(2,480:0.0000ObE^R-squarcd43.78138Prob.Chi-Square(3)0.0000TestEquation:DependsntVariable:RESIDE「」ethod:LeastSquares□ate:12/17；14Time:09:54Sampl已(adjusted；:5488Includedobservations:484afteradjustmentsCoefficientStd.Errort-8tatisticProb.C17922.522905.3126.1686730.0000RESIDA^i：-1>0.2671590.04-646552497060.0000RESIDA2t-2)0.1060010.04^8402.2157300.0272RESID^t-3)-0.05Eia60.04-6454-1.1862340.2361R-squared0.090457LIeandependentwar2603797Adjusted尺-2口uared0.0S4^73S.D.dependentvar5576543S.E.ofregression53349.40Akaikeinfo匚riterion24.01534Sumsquaredresid1.37E+12Schwarzcriterion24.64991LogliKeliliood-5952.913Hannan-Quinncriter.24.'52692F-statistic15.91259Durbin-Watsonstat1.960203Prob(F-statistic;o.ooooao從圖中可以看出，F(xiàn)統(tǒng)計量和LM統(tǒng)計量都是顯著的，說明方程殘差項具有ARCH效應(yīng)。2、對AS做單方程的GARCH估計Dependent'Variable:ISMethod:I.1L-ARCH^.larquardt;-MormaldistributionDate:12/VH4Time:09:5BSample[adjiiEted):3488Includedobservations:480afteradjustmentsConvergenceachieved3fler40iterationsPresample-zariance:baclxast(paramete「二0.7)GARCH=Ci：4)^CC5rRESID(-1：r2^Ci：5rGARCH(-1；CoefficientSid.Error^-StatisticProb.c-30.13214254-1994-1.1052740.235019(-1}0.0208030.0570750.3644S207155E(-1}-0.4344770.10^'370-4,0730810.0000''■/arian匚rEquationC13022.312963.2204.3946480.0000RESID(-ir20.1559790.0223986.9640200.0000GARCHi-T；0.820004Q.02250036.444530.0000R-squared0.029368[Jeandep?nden-40.S6420AdjustedR-squared0.019257SlD..dependantvatG40.7924S.E.ofregr&ssioi-!634.5934Akaikeinfocriterion15.40896SumsquaredreEid1.93E+08Schwarzcriterion15.46065Loglikelihood-3738.378Hannan-Cuinncriter.15.42627F-statistic2.904626Durbin-Watsonstat1.887693Prob(F-statistic;0.013520GARCH012,800,000-|2,400.000-2.000.000-I1.600.000501001502002503003504004503、對AF做單方程的GARCH估計Dependentvariable:IFDependentvariable:IFMethod:f,1L-.ARCHCMarquardt':-Norin31distribution□ate:12/V/14Time:09:59Sample[adjushed):3488Includedobservations:4B6afteradjustmentsDependentvariable:IFDependentvariable:IFMethod:f,1L-.ARCHCMarquardt':-Norin31distribution□ate:12/V/14Time:09:59Sample[adjushed):3488Includedobservations:4B6afteradjustmentsCon-^ergenceachieved3fler42iterationsPresample-yariance:backcast(parameter=0.7}G.ARCH二C(4)+■Ct5rRESID(-1^2+■Ci'jrGARCH(-1；CoefficientStd.Errorz-StatisticProb.G-19.038702^.53577-0.6914170.4893IFH)0.0201020.0557140.360B09Q7182E(-1；-0.1504070.119795-1.2554990.2093VarianceEquationC10591.1130907^82.4265793.0006RESID(-ir20.1175800.0204595.7470S9a.ooaoGARCHi-1；0.8614650.02135740.33586a.ooaoR-squared0.005167L1eanCependentvar-40.14403Adjusteg!R-squa「Ed-0.00^195S.D.dependentvar6S1.4727S.E.ofregression■363.1888Akaikeinfocriterion15.53035Sumsciuaredresid211E+D8SchwarzeritEeiori15.58203Loglivelihood-3767.S75Hannan-Quinncriter.15.55065F-siatistic0.498646Durbin-Watsonstat1.S71284Prob(F-statistic;07^^3184、計算動態(tài)最優(yōu)套期保值比率對兩個GARCH模型的殘差進行比較RESID01RESID02RESID011.0000000.966342RESIDC20.9663421.000000得到動態(tài)最優(yōu)套期保值比率，具體數(shù)據(jù)見附錄2還可得最優(yōu)套期保值序列的均值和標(biāo)準(zhǔn)差HMean0.912039Median0.911467Maximum1.1-35449Minimum0.B72511Std.Dev.0.065131Skewness0.09796SKurtosis4.5428C37Jarque-Bera46.9772F7Probabilit;0.0000007Sum443.25087SumSq.Dev.2.057423Obsenrations4甜寸五）對利用最小方差套期比的套保組合進行績效評估通過OLS,CM模型估計出的最優(yōu)套期保值比分別為0.931627,0.924849,ECM-BGARCH模型計算出的最優(yōu)套期保值比率均值為0.912039?，F(xiàn)在我們用上述三個套期保值比套期保值的組合和沒有經(jīng)過套期保值的現(xiàn)貨收益率進行方差比較,這里收益用價格相對變化表示。OLS下套期保值效果的統(tǒng)計性描述：

Series:P1Sample2Series:P1Sample24-88Ohservations487L1ean-0.001578Median-9.45E-06Llaxinuim0.142S85Hinimuin-0.183433Std.Dev.0.039302Skewness-0789869Kurtosis7.279717Jarque-Bera422.3011Probabilitya.oooaoo由Std.Dev.后面的數(shù)據(jù)0.039302，說明序列P1標(biāo)準(zhǔn)差=0.039302ECM下套期保值效果的統(tǒng)計性描述：Series:P2Series:P2Sample248SObsen/ati-jns48?Mean-0.004746Lledian0.000858Llaximum0.308026LIinimum-0.442375Std.Dev.O.OSS666Skewnms；s-0.980687KurtosiE7.177920Jarque-Bera432.2549Probability0.000000由Std.Dev.后面的數(shù)據(jù)0.088666，說明序列P2標(biāo)準(zhǔn)差=0.088666無保值組合的統(tǒng)計性描述：Series:P-SampIs-Z4&BGtserYaiicrs4￡7Mean-C.CCC7?1Msdiain0.00C216MaximumC.C-6C77Llinimum-C.CcCSZBStd.D&v.0.01C9MSkewness-1.162^?CKcrtcsis11.0^795Jarque-BeraPrcbabil^0.000000由Std.Dev.后面的數(shù)據(jù)0.010985，說明序列P3標(biāo)準(zhǔn)差=0.010985將上述所得結(jié)果列入下表中進行比較，如下所示：表2-1不同方法下套期保值效果比較OLS模型套保組合ECM模型套保組合ECM-BGARCH模型套保組合未經(jīng)過套保套保組合套期保值比率0.9316270.9248490.912039（均值）0組合收益率標(biāo)準(zhǔn)差0.0393020.0886660.0651310.010985從表2-1中可以看出，（1）經(jīng)過套期保值的組合收益率方差都比未經(jīng)過套期保值的收益率，說明用期貨套期保值是有效的；（2）利用ECM-BGARCH模型進行套期保值的組合收益率的方差最小，能最大限度的降低價格風(fēng)險，在用于測算最優(yōu)套期保值比時更精確。四、實驗總結(jié)上機讓我更深入的想去理解套期保值，如果進行套期保值，可以在期貨市場上做一定比例的反向操作，期貨市場上價格波動帶來的收益用以彌補現(xiàn)貨市場上由于價格波動所造長的虧損，從而達(dá)到控制成本、鎖定利潤的效果。五、附錄

附錄1日期期貨價格f現(xiàn)貨價格s日期期貨價格f現(xiàn)貨價格s日期期貨價格f現(xiàn)貨價格s日期期貨價格f現(xiàn)貨價格s2011/4/1871160713752011/10/1756410564252012/4/1857370571752012/10/1758740586152011/4/1970340706002011/10/1854780551002012/4/1957470574252012/10/1859140590252011/4/2071150714002011/10/1954900554502012/4/2057720575702012/10/1958750588002011/4/2171680718252011/10/2051930524752012/4/2357640576302012/10/2257950579202011/4/2271680718252011/10/2152380531502012/4/2457540575302012/10/2357720577602011/4/2571270715252011/10/2454530544502012/4/2557600575902012/10/2457390577602011/4/2670320706502011/10/2556790572002012/4/2657890577752012/10/2557400572252011/4/2770550709502011/10/2657160573252012/4/2758360582752012/10/2657070570252011/4/2870730711752011/10/2757860580752012/5/258580585002012/10/2956880568502011/4/2970060705502011/10/2860280601752012/5/358270583402012/10/3056410563502011/5/370070704752011/10/3158980593502012/5/458180582352012/10/3156720566002011/5/469460701002011/11/158930595252012/5/757660577502012/11/156860566202011/5/568420690002011/11/258310583252012/5/857940580302012/11/256750567002011/5/667240675352011/11/357980582502012/5/957530576402012/11/555770558002011/5/967790680502011/11/459230590752012/5/1057750577502012/11/655960559202011/5/1067740680752011/11/758620586252012/5/1157720578002012/11/756270562002011/5/1167870679002011/11/858350583252012/5/1457110571502012/11/855630557052011/5/1266100665002011/11/958840587502012/5/1556060562002012/11/955980560352011/5/1366710666002011/11/1055790559752012/5/1655660560002012/11/1255460554252011/5/1666790669002011/11/1156340564502012/5/1755840558352012/11/1355460555752011/5/1766350666252011/11/1458730587002012/5/1855730560502012/11/1455650557102011/5/1867210675502011/11/1558280582502012/5/2156410566502012/11/1555610556502011/5/1967990684252011/11/1657500580502012/5/2256480567402012/11/1655800558502011/5/2067620680752011/11/1757080574752012/5/2355680560752012/11/1955980559752011/5/2367210681502011/11/1856190569252012/5/2455530558402012/11/2056310562852011/5/2467150677002011/11/2156040567752012/5/2555750559002012/11/2156000560502011/5/2567680683502011/11/2255850566002012/5/2856540566352012/11/2255990559752011/5/2668530690002011/11/2355800565252012/5/2956500566252012/11/2355710557002011/5/2768700690252011/11/2454770554002012/5/3055960561252012/11/2656090560402011/5/3068860694002011/11/2554910555752012/5/3155180554252012/11/2756260561552011/5/3168950694102011/11/2856140568752012/6/155170554752012/11/2856200561352011/6/169120694252011/11/2956160566002012/6/453850543502012/11/2956220560752011/6/268390687752011/11/3055760562752012/6/554260546252012/11/3056750565102011/6/368940693502011/12/158340584002012/6/654450547752012/12/357310571252011/6/769000695502011/12/258170583502012/6/754730550902012/12/457110570002011/6/868650690002011/12/558370585252012/6/854110544502012/12/557330571752011/6/968600690252011/12/657940580102012/6/1155170553502012/12/657280573002011/6/1068600691002011/12/758400582502012/6/1254540547402012/12/757030570352011/6/1368170685002011/12/858160580352012/6/1354760548402012/12/105742057300

2011/6/1468380685502011/12/957790578002012/6/1454850549002012/12/1157470574902011/6/1569370693252011/12/1257480574752012/6/1555440554002012/12/1257400574302011/6/1668680691502011/12/1356700565502012/6/1855620557502012/12/1357140571902011/6/1768770690752011/12/1456370565502012/6/1955570558902012/12/1457170572802011/6/2068280688002011/12/1554850545502012/6/2055710559902012/12/1757350574252011/6/2168380686002011/12/1654450545502012/6/2155020554202012/12/1857700575802011/6/2268340687752011/12/1954270543752012/6/2554880552502012/12/1957400571852011/6/2367920683502011/12/2054340544202012/6/2654720552502012/12/2056750564502011/6/2468640686752011/12/2155130551502012/6/2754370545902012/12/2156480561252011/6/2768000683752011/12/2255110550002012/6/2854780548002012/12/2456750564852011/6/2868260683502011/12/2355940555752012/6/2955250549502012/12/2556690563802011/6/2968710685252011/12/2655750554502012/7/255830558252012/12/2656830565252011/6/3069770694252011/12/2755550551402012/7/356160560502012/12/2757120567502011/7/170080698252011/12/2855710552252012/7/456290562902012/12/2857170567902011/7/470780706502011/12/2954860546252012/7/556210562502012/12/3157200571002011/7/570480704752011/12/3055460552502012/7/656050560502013/1/457710575252011/7/670780707002012/1/456580563002012/7/955550556002013/1/757640575602011/7/770980708752012/1/556110558752012/7/1055600557302013/1/857710575802011/7/871960718502012/1/655940559002012/7/1155450555652013/1/957640575402011/7/1171640717102012/1/955440553252012/7/1255440554752013/1/1057760576702011/7/1271130711352012/1/1055480552252012/7/1355720556752013/1/1157940579702011/7/1371790716002012/1/1155990558252012/7/1656100560752013/1/1457730577252011/7/1471750716802012/1/1256450563002012/7/1756070562652013/1/1557350575502011/7/1571570716252012/1/1357080570502012/7/1855790559002013/1/1657630575002011/7/1871900718802012/1/1657460571252012/7/1956030560252013/1/1757430573002011/7/1972090718252012/1/1758920581752012/7/2056120562802013/1/1857860576502011/7/2072340720502012/1/1859090585502012/7/2355110553202013/1/2157840577102011/7/2172040719002012/1/1959760594252012/7/2454910550352013/1/2258130579502011/7/2271660715202012/1/2060290599002012/7/2554610549252013/1/2358150579752011/7/2571500714102012/1/3060300602002012/7/2654560546852013/1/2458050579152011/7/2671980717252012/1/3159750594502012/7/2754870549252013/1/2558270581202011/7/2772300719502012/2/159160590252012/7/3055370555302013/1/2858070579352011/7/2872210718502012/2/259170589002012/7/3155260553502013/1/2958130579352011/7/2972480721252012/2/359130589002012/8/155060551302013/1/3058580584252011/8/172490723252012/2/659920596502012/8/254930550152013/1/3158880586002011/8/271880717002012/2/759520594502012/8/354450545102013⑵158830586102011/8/371880717252012/2/860100595002012/8/654920550002013/2/459320590852011/8/471400714002012/2/960530605502012/8/755080552252013/2/559070588752011/8/569310695752012/2/1060710606702012/8/855320554752013/2/659090590002011/8/867940681752012/2/1359780597502012/8/955460555902013/2/758730587002011/8/965050647502012/2/1459310594252012/8/1055320555202013/2/858930589602011/8/1067080670752012/2/1559550595752012/8/1355260553202013⑵185848058590

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