I want to forecast a differenced time series of an Index using the combined ARMA-GARCH model (because I want to forecast the mean and not the variance). My model is a ARMA(2,2)-GARCH(1,1) model. So the equations for the first forecast are:
Y(t+1)=Y(t)+Alpha(1)*(Y(t)-Y(t-1))+Alpha(2)*(Y(t-1)-Y(t-2)) - Beta(1)*e(t) - Beta(2)*e(t-1) + e(t+1) with
e(t+1) = Sigma(t+1)*Z(t+1) , Z(t+1)=N(0,1) and
Sigma^2 (t+1) = Omega + a(1)*u^2(t) + b(1)*Sigma^2(t) I tried it with the "rugarch" package and the ugarchforecast method:
GARCHspec <- ugarchspec( variance.model = list(model = "sGARCH", garchOrder = c(1, 1)),mean.model = list(armaOrder = c(2, 2), include.mean = TRUE)) GARCHfit <- ugarchfit(GARCHspec, diffclosingkursu) ugarchforecast(GARCHfit,n.ahead=250) The forecast seems to be quite strange
*------------------------------------* * GARCH Model Forecast * *------------------------------------* Model: sGARCH Horizon: 30 Roll Steps: 0 Out of Sample: 0 0-roll forecast [T0=1976-11-23 01:00:00]: Series Sigma T+1 10.28 0.7802 T+2 10.30 0.8580 T+3 10.32 0.9264 T+4 10.34 0.9876 T+5 10.36 1.0429 T+6 10.38 1.0933 T+7 10.40 1.1395 T+8 10.43 1.1822 T+9 10.45 1.2217 T+10 10.47 1.2585 T+11 10.49 1.2927 T+12 10.51 1.3247 T+13 10.54 1.3547 T+14 10.56 1.3829 T+15 10.58 1.4093 T+16 10.60 1.4343 T+17 10.63 1.4578 T+18 10.65 1.4800 T+19 10.67 1.5010 T+20 10.69 1.5208 T+21 10.71 1.5396 T+22 10.74 1.5574 T+23 10.76 1.5743 T+24 10.78 1.5903 T+25 10.80 1.6056 T+26 10.82 1.6200 T+27 10.85 1.6338 T+28 10.87 1.6469 T+29 10.89 1.6593 T+30 10.91 1.6743 Also, how can it be, that every forecast for the same time series is the same but e(t+1) should be a random variable?
EDIT:
ugarchspec( variance.model = list(model = "sGARCH", garchOrder = c(1, 1)),mean.model = list(armaOrder = c(2, 2), include.mean = TRUE)) *---------------------------------* * GARCH Model Spec * *---------------------------------* Conditional Variance Dynamics ------------------------------------ GARCH Model : sGARCH(1,1) Variance Targeting : FALSE Conditional Mean Dynamics ------------------------------------ Mean Model : ARFIMA(2,0,2) Include Mean : TRUE GARCH-in-Mean : FALSE Conditional Distribution ------------------------------------ Distribution : norm Includes Skew : FALSE Includes Shape : FALSE Includes Lambda : FALSE ugarchfit(GARCHspec, closingkursu) *---------------------------------* * GARCH Model Fit * *---------------------------------* Conditional Variance Dynamics ----------------------------------- GARCH Model : sGARCH(1,1) Mean Model : ARFIMA(2,0,2) Distribution : norm Optimal Parameters ------------------------------------ Estimate Std. Error t value Pr(>|t|) mu 24.000332 0.774681 30.98091 0.00000 ar1 1.795379 0.000456 3935.65863 0.00000 ar2 -0.795715 0.000360 -2209.69523 0.00000 ma1 -0.891371 0.026093 -34.16102 0.00000 ma2 0.008179 0.024713 0.33094 0.74069 omega 0.152244 0.020684 7.36031 0.00000 alpha1 0.229838 0.023339 9.84798 0.00000 beta1 0.729204 0.023038 31.65283 0.00000 Robust Standard Errors: Estimate Std. Error t value Pr(>|t|) mu 24.000332 0.644910 37.21499 0.000000 ar1 1.795379 0.000731 2456.77236 0.000000 ar2 -0.795715 0.000471 -1687.85596 0.000000 ma1 -0.891371 0.035477 -25.12542 0.000000 ma2 0.008179 0.030585 0.26741 0.789156 omega 0.152244 0.040365 3.77172 0.000162 alpha1 0.229838 0.043371 5.29932 0.000000 beta1 0.729204 0.041929 17.39135 0.000000 LogLikelihood : -4427.411 Information Criteria ------------------------------------ Akaike 3.5230 Bayes 3.5415 Shibata 3.5229 Hannan-Quinn 3.5297 Weighted Ljung-Box Test on Standardized Residuals ------------------------------------ statistic p-value Lag[1] 0.1058 0.7449 Lag[2*(p+q)+(p+q)-1][11] 1.6123 1.0000 Lag[4*(p+q)+(p+q)-1][19] 5.5053 0.9859 d.o.f=4 H0 : No serial correlation Weighted Ljung-Box Test on Standardized Squared Residuals ------------------------------------ statistic p-value Lag[1] 0.01876 0.8911 Lag[2*(p+q)+(p+q)-1][5] 4.39776 0.2084 Lag[4*(p+q)+(p+q)-1][9] 6.38265 0.2566 d.o.f=2 Weighted ARCH LM Tests ------------------------------------ Statistic Shape Scale P-Value ARCH Lag[3] 1.227 0.500 2.000 0.2681 ARCH Lag[5] 2.548 1.440 1.667 0.3623 ARCH Lag[7] 3.489 2.315 1.543 0.4262 Nyblom stability test ------------------------------------ Joint Statistic: 2.1259 Individual Statistics: mu 0.01831 ar1 0.34437 ar2 0.33567 ma1 0.06716 ma2 0.02932 omega 0.44909 alpha1 0.79120 beta1 1.02912 Asymptotic Critical Values (10% 5% 1%) Joint Statistic: 1.89 2.11 2.59 Individual Statistic: 0.35 0.47 0.75 Sign Bias Test ------------------------------------ t-value prob sig Sign Bias 3.3842 0.0007247 *** Negative Sign Bias 0.5320 0.5947678 Positive Sign Bias 0.3897 0.6967648 Joint Effect 19.5500 0.0002104 *** Adjusted Pearson Goodness-of-Fit Test: ------------------------------------ group statistic p-value(g-1) 1 20 259.6 3.576e-44 2 30 286.2 4.244e-44 3 40 322.5 2.575e-46 4 50 348.2 1.040e-46 Elapsed time : 0.30832 Best Answer
The forecast seems to be quite strange
What exactly do you mean? What is the question here?
Also, how can it be, that every forecast for the same time series is the same but
e(t+1)should be a random variable?
The best point forecast for the error term e(t+1) under square loss is its estimated conditional mean, which is zero. You do not expect e(t+1) to actually be zero, but zero is your best guess. That is why the point forecast for e(t+1) is zero every time.
My goal is not to find the best guess but to model a time series model (at least that is what my professor said). So is there a way to set
e(t+1)equal to a random variable with zero mean and the conditional variance instead of zero?
Your question title says "forecasting", so I answered it as such. Now if you want to model the time series for some other purpose, you can still use the same model as an approximation of the true data generating mechanism. If you want to simulate some paths from your estimated model, you can do that with the functions ugarchsim and/or ugarchpath. They will generate some random errors according to the estimated properties of those errors from the historical data.
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