发布时间 : 星期四 文章时间序列实验报告3更新完毕开始阅读0d00b73d66ec102de2bd960590c69ec3d5bbdb34
时间序列分析实验报告
Problem1:Estimate ARMA-ARCH model for financial series in arch序列.xls
? create new integer-data workfile named arch1,import the data series named y ? Estimate AR model by correlogram--eq01
? Diagnostic checks :test whether there is serial correlation in the residuals by Q-statistics and LM –test.
? Test heteroskedasticity, give your reason briefly
? Establish AR-ARCH(q) model for possible order q , and select the best one as your final model (note: parameters, whether there is remaining ARCH effect in standardized residuals and information criterions should be considered) ? Write out the mean equation and variance equation---eq02
Problem2 : Estimate ARMA-TGARCH(EGARCH) model for financial series in杠杆数据.xls ? create a new integer-data workfile named杠杆; import data series named y ? Estimate ARMA model by correlogram----eq01—
(sometimes the significance of coefficient can be omitted temporarily)
①. Diagnostic checks :test whether there is serial correlation in the residuals by
Q-statistics and LM –test.
②. Test heteroskedasticity(null hypothesis(H0): there is no ARCH in the residuals)
? Correlogram of squared residuals ----Q-statistic
? ARCH-LM test : (In the Lag Specification dialog box you should specify
the lag order)
? Establish ARMA-TGARCH--eq02, check whether there is leverage effect ,give your reason:
? diagnostic checking on standardized residuals of eq02. ? Write out mean equation and variance equation of eq02 ?
You can try to establish ARMA-EGARCH model，check whether there is leverage effect and give your reason: ---eq03
? diagnostic checking on standardized residuals of eq03. ?
Write out mean equation and variance equation---eq03
实验报告结果
Yt=0.477*Yt-1 - 0.208* Yt-2 +Ut
（1-0.447*L+0.208*L^2）Yt=Ut
which is calculated with 12 correlation coefficients (and 10
degrees of freedom) is Q(12) =12.101. Since p value (=0.278) is larger than 0.05, there is no serial correlation in the residuals under the 5 percent level
Establish an AR(1)-ARCH(1) model Yt=0.439Yt-1+ξt
Ht^2=1.133+0.980ξt-1^2
the corresponding p value is 0.318. This implies that the squared standardized residuals are not auto correlated. (12) 11.525Q?
(3.2) In LM test, the value of the test statistic is LM(2)=0.041,and the co80rresponding p value is 0.980.This implies that there exists no ARCH effect in the standardized residuals. ?^2=1.113/(1-0.980)=56.650
通过指定 LM 检验滞后的阶数为 2，发现残差中不存在自相关性，截图如下：
(2.2) 通过对残差平方的 LBQ 检验发现，残差平方中存在自相关性，截图如下：
（2.3）通过在 ARCH-LM 检验中指定滞后的阶数为 2 发现，条件异方差性存在
班级 姓名 学号
(3.3) Check whether there is leverage effect. Please give your reason briefly. 建立 AR(1)-TARCH(1,1)模型。
(3.1) (1? 0.871L) yt ? ?t ， ht ? 0.083 ? 0.226?t ?1 ? 0.611?t ?1dt ?1 ? 0.460ht ?1
(3.2) 通过对标准化残差的 LBQ 检验发现，标准化残差中不存在自相关性，截图如下：
通过对标准化残差平方的 LBQ 检验发现，标准化残差平方之间不存在自相关性，截图 如下： (3.3) The TARCH(1,1) model is
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