MODEL 1 Dependent Variable: D_DMI Method: ML ARCH- Normal distribution (BFGS/ Marquardt steps) Date: 08/07/20 Time: 11:1

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Model 1 Dependent Variable D Dmi Method Ml Arch Normal Distribution Bfgs Marquardt Steps Date 08 07 20 Time 11 1 1 (130.2 KiB) Viewed 12 times

MODEL 1 Dependent Variable: D_DMI Method: ML ARCH- Normal distribution (BFGS/ Marquardt steps) Date: 08/07/20 Time: 11:13 Sample (adjusted): 31500 Included observations: 1498 after adjustments Convergence achieved after 12 iterations. Coefficient covariance computed using outer product of gradients Presample variance: backcast (parameter = 0.7) GARCH=C(3) + C(4)*RESID(-1)^2 Variable C AR(1) C RESID(-1)^2 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots MODEL 3 Coefficient Std. Error z-Statistic -0.000393 0.000184 -2.139335 0.0324 -0.067079 0.028668 -2.339882 0.0193 Variance Equation 4.68E-05 1.77E-06 26.41991 0.0000 0.194876 0.021639 9.005742 0.0000 0.002655 Mean dependent var 0.001988 S.D. dependent var 0.007599 Akaike info criterion 0.086376 Schwarz criterion 5211.347 Hannan-Quinn criter. 1.969580 Dependent Variable: D_DM Method: ML ARCH- Normal distribution (BFGS/ Marquardt steps) Date: 08/07/20 Time: 11:15 -.07 Sample (adjusted): 31500 Included observations: 1490 after adjustments Convergence achieved after 33 iterations Coefficient covariance computed using outer product of gradients C RESID(-1)^2 RESID(-1)ID(-1)-0) GARCH(-1) R-squared Adjusted R-squared SF of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4) RESID(-1)^2 + C(5) RESID(-1)^2*(RESID(-1)=0) + C(0) OARCH(-1) Variable с AR(1) Coefficient Std. Error 2-Statistic Prob. -0.000520 0.000160 -3.247433 0.0012 -0.079006 0.028584 -2.785013 0.0054 Prob. -0.000258 0.007606 -6.952399 -6.938215 -6.947115 0.001191 Mean dependent var 0.000523 S.D. dependent var 0 007604 Akaike info criterion 0.086503 Schwarz criterion 5299.392 Hannan-Quinn criter. 1.941218 Variance Equation 7.60E-07 2.70E-07 2.809456 0.0050 0.065232 0.011844 5.507768 0.0000 0.059687 0.016741 3.565283 0.0004 0.899439 0.013137 68.46483 0.0000 -.08 -0.000258 0.007606 -7.067279 -7.046003 -7.059352 MODEL 2 8 Dependent Variable: D_DM Method: ML ARCH- Normal distribution (BFGS/Marquardt steps) Date: 08/07/20 Time: 11:14. Sample (adjusted): 31500 Included observations: 1498 after adjustments Convergence achieved after 27 iterations Coefficient covariance computed using outer product of gradients Presample variance: backcast (parameter = 0.7) GARCH=C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) Variable с AR(1) с RESID(-1)^2 GARCH(-1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted AR Roots Std. Error z-Statistic Coefficient -0.000412 0.000148 -2.780930 0.0054 -0.077550 0.029167 -2.658784 0.0078 Variance Equation 1.19E-06 3.42E-07 3.476185 0.107640 0.012296 8.753966 0.877283 0.014597 60.10056 0.002196 Mean dependent var 0.001529 S.D. dependent var 0.007600 Akaike info criterion 0.086416 Schwarz criterion 5294.881 Hannan-Quinn criter. 1.947314 -.08 Prob. 0.0005 0.0000 0.0000 -0.000258 0.007606 -7.062591 -7.044861 -7.055986 III. Based on the Three models of the ARCH/GARCH type in part (II) above, explain whether good and bad news have symmetric effects on D_DM. (2 Marks)