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As the important tool of network finance, YU’EBAO has highly financial product yield, liquidity and other advantages compared with bank. But accompanied by such financial products innovation, their earnings also gradually fell back. By Eviews 6.0 ,the paper studies YU’EBAO annual yields from May 30, 2013 to September 5, 2014 , establishes the financial time series of the ARCH model, compares and concludes T – GARCH better fitting of the volatility of its annual yield condition, the paper would help network investors forecast the earnings trend. The introduction Financial time series data analysis exists the common rule that the variance of random perturbation terms ut often accompanies the changes and integration of variance of fitting function over time, and which go against classical econometric analysis model of variance for constant. That using such as least squares classical econometric model to predict the future development trend will seriously affect the prediction effect. Therefore, at the beginning of Robert F.Engle proposeing the ARCH model, Bollerslev, Nelson, Zakoizn, Robbins, and so on ,they all further develop the ARCH model to provide some theoretical support for predicting the stock market returns, inflation, foreign currency exchange rate changes. As a representative of the emerging network products, since early June 2013 on taobao, YU’EBAO has been favored by the majority of network financial investors, the present yields characteristics is from high to low. To help such investors, the article will use the autoregressive conditional heteroscedastic model to empirically analyze the trends of YU’EBAO annual yields. The empirical analysis and prediction Data Selection. The article selects the dates of Tianhong increased profits treasure money market funds (funds code: 000198) from May 30, 2013 to September 5, 2014, which statistics 398 precious date annual return of the balance of the observed value. We assume the date annual return is Rt (%), t ∈ [1,398].For reducing the changes of Rt, LOG (Rt) is on behalf of the logarithmic benefits. The selection of regression equation lags order. For accurately judging estimated parameters of the lag order number, the paper chooses Akaike information criterion (AIC) and the schwarz information criterion (SC) to determine the optimal lag order number, from table 1, the results can be judged by the lag order 6, where AIC and SC is minimal and the lag order number is ideal. Table 1 The lag order selection based on the regression equation of the AIC and SC lag AIC SC lag AIC SC One -5.136760 -5.126725 Five -6.626635 -6.576078 Two -6.113170 -6.093062 Six -6.692236 -6.631451 Three -6.409332 -6.379113 Seve -6.692199 -6.621148 Four -6.554477 -6.514108 Heteroscedasticity regression equation. According to the choice of the lag order, we create the logarithm regression equation as follows. LOG(R) = 1.59774*LOG(R(-1)) 0.41190*LOG(R(-2)) 0.12641*LOG(R(-3)) + standard deviation (0.123706) (0.197523) (0.097423) 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA 2016) © 2016. The authors Published by Atlantis Press 201 T statistics (12.91562) (-2.085311) (-1.297563) 0.00774*LOG(R(-4)) 0.18151*LOG(R(-5)) + 0.11436*LOG(R(-6)) (0.078997) (0.071030) (0.034735) (0.097991) (-2.555455) (3.292508) R2=0.996903;A-R2=0.996863;DW=2.011195; SE=0.008458;LOG LIKELI HOOD =1317.678. Because LOG (R3), LOG (R4) was not significant in the matter t statistics, the regression results are shown in table 2. Table 2 the LS regression results rejecting the LOG (R3), LOG (R4) Although regression R = 0.996876, which represents higher fitting degree; Logarithmic likelihood function value,1317 is bigger, which represents the model is more accurate; DW is 2.064887, which delegates the first-order autocorrelation is non-existent, however, whether the correlation regression equation residual exists higher-order autocorrelation, it is necessary to continue to use the test of residual graphical inspection, the ARCH LM test, Normality test, white inspection, different variance, the results found that there were different variance phenomenon. Residual figure shows residual volatile concentration areas, namely, behind the large fluctuations, there are a series of more substantial fluctuations, and also, behind the fluctuations of the small, there are a series of small amplitude fluctuations. So, it is refused to residual autocorrelation hypothesis. ARCH LM test are shown in table 3, the F statistics and P values can be seen that residual error has a serious self-correlation. Table 3 Heteroskedasticity Test: ARCH Normality test results show that the characteristic of "rush thick tail" is obvious, the peak Kurtosis is 17.11908, the Skew ness is 1.326976, residual autocorrelation hypothesis should be accepted. Heteroscedasticity test are shown in table 4, which also reject the hypothesis that there is no residual autocorrelation. Coefficient Std. Error t-Statistic Prob. LOG(R(-1)) 1.627108 0.113288 14.36264 0.0000 LOG(R(-2)) -0.534181 0.146154 -3.654917 0.0003 LOG(R(-5)) -0.197574 0.068806 -2.871473 0.0043 LOG(R(-6)) 0.104659 0.037072 2.823105 0.0050 R-squared 0.99688 Mean dependent var 1.601927 Adjusted R-squared 0.99685 S.D. dependent var 0.151005 S.E. of regression 0.00847 Akaike info criterion -6.693925 Sum squared resid 0.02785 Schwarz criterion -6.653402 Log likelihood 1316.01 Hannan-Quinn criter. -6.677865 Durbin-Watson stat 2.06489 F-statistic 54.27476 Prob. F(1,389) 0.0000 Obs*R-squared 47.87422 Prob. Chi-Square(1) 0.0000

The GARCH Analysis of YU'EBAO Annual Yields