Analisis Pengaruh Kebijakan Moneter Melalui Instrumen Suku Bunga Terhadap Return Saham Di Bursa Efek Indonesia

Lampiran Lampiran 1

Model GARCH Indeks Harga Saham Gabungan GARCH (1,0)

Dependent Variable: IHSG

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/06/14 Time: 12:12

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 28 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.086003 0.069119 1.244275 0.2134 BIRATE -0.009611 0.008849 -1.086069 0.2774

Variance Equation

C 0.001201 0.005723 0.209896 0.8337 GARCH(-1) 0.775965 1.083006 0.716492 0.4737 R-squared 0.030180 Mean dependent var 0.015622 Adjusted R-squared -0.013245 S.D. dependent var 0.073930 S.E. of regression 0.074418 Akaike info criterion -2.310063 Sum squared resid 0.371049 Schwarz criterion -2.182588 Log likelihood 86.00725 Hannan-Quinn criter. -2.259371 F-statistic 0.695000 Durbin-Watson stat 1.512131 Prob(F-statistic) 0.558331

GARCH (1,1)

Dependent Variable: IHSG

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/06/14 Time: 12:14

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 50 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.009327 0.054092 0.172434 0.8631

BIRATE 0.001241 0.007504 0.165332 0.8687 Variance Equation

C 0.002544 0.001496 1.701171 0.0889 RESID(-1)^2 0.548411 0.204498 2.681739 0.0073 GARCH(-1) 0.021620 0.328406 0.065833 0.9475 R-squared -0.008494 Mean dependent var 0.015622 Adjusted R-squared -0.069615 S.D. dependent var 0.073930 S.E. of regression 0.076460 Akaike info criterion -2.461484 Sum squared resid 0.385846 Schwarz criterion -2.302141 Log likelihood 92.38269 Hannan-Quinn criter. -2.398118 Durbin-Watson stat 1.456703

GARCH (1,2)

Dependent Variable: IHSG

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/06/14 Time: 12:15

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 29 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.006232 0.053972 0.115464 0.9081 BIRATE 0.001707 0.007452 0.229130 0.8188

Variance Equation

C 0.000584 0.001527 0.382260 0.7023 RESID(-1)^2 0.510401 0.194164 2.628714 0.0086 RESID(-2)^2 -0.367607 0.404342 -0.909151 0.3633 GARCH(-1) 0.758176 0.633097 1.197568 0.2311 R-squared -0.011669 Mean dependent var 0.015622 Adjusted R-squared -0.089489 S.D. dependent var 0.073930 S.E. of regression 0.077167 Akaike info criterion -2.443148 Sum squared resid 0.387060 Schwarz criterion -2.251935 Log likelihood 92.73174 Hannan-Quinn criter. -2.367108 Durbin-Watson stat 1.452272

GARCH (2,1)

Dependent Variable: IHSG

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/06/14 Time: 12:16

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 43 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.010826 0.052230 0.207276 0.8358 BIRATE 0.000982 0.007258 0.135231 0.8924

Variance Equation

C 0.002271 0.001439 1.578645 0.1144 RESID(-1)^2 0.539207 0.196848 2.739201 0.0062 GARCH(-1) -0.005104 0.354046 -0.014417 0.9885 GARCH(-2) 0.084587 0.203010 0.416667 0.6769 R-squared -0.006597 Mean dependent var 0.015622 Adjusted R-squared -0.084028 S.D. dependent var 0.073930 S.E. of regression 0.076974 Akaike info criterion -2.435691 Sum squared resid 0.385120 Schwarz criterion -2.244478 Log likelihood 92.46702 Hannan-Quinn criter. -2.359652 Durbin-Watson stat 1.459370

Model GARCH Saham Sektor Aneka Industri GARCH(1,0)

Dependent Variable: ANEKAINDUSTRI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:29

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 16 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.098081 0.091005 1.077754 0.2811

BIRATE -0.009625 0.011835 -0.813285 0.4161 Variance Equation

C 0.002065 0.006466 0.319377 0.7494 GARCH(-1) 0.803196 0.642037 1.251013 0.2109 R-squared 0.013691 Mean dependent var 0.028048 Adjusted R-squared -0.030472 S.D. dependent var 0.102021 S.E. of regression 0.103564 Akaike info criterion -1.655629 Sum squared resid 0.718611 Schwarz criterion -1.528154 Log likelihood 62.77481 Hannan-Quinn criter. -1.604936 F-statistic 0.310006 Durbin-Watson stat 1.854384 Prob(F-statistic) 0.818070

GARCH(1,1)

Dependent Variable: ANEKAINDUSTRI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:30

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 106 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.011337 0.066575 0.170288 0.8648 BIRATE 0.002211 0.009031 0.244806 0.8066

Variance Equation

C 0.005971 0.003788 1.576458 0.1149 RESID(-1)^2 0.546283 0.221401 2.467392 0.0136 GARCH(-1) -0.058161 0.392222 -0.148286 0.8821 R-squared -0.006587 Mean dependent var 0.028048 Adjusted R-squared -0.067592 S.D. dependent var 0.102021 S.E. of regression 0.105413 Akaike info criterion -1.741102 Sum squared resid 0.733385 Schwarz criterion -1.581758 Log likelihood 66.80912 Hannan-Quinn criter. -1.677736 Durbin-Watson stat 1.818428

Dependent Variable: ANEKAINDUSTRI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:31

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 21 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.096869 0.085519 1.132724 0.2573 BIRATE -0.009774 0.011691 -0.836035 0.4031

Variance Equation

C 0.005444 0.003080 1.767407 0.0772 RESID(-1)^2 0.226526 0.144371 1.569050 0.1166 RESID(-2)^2 -0.132073 0.104976 -1.258125 0.2083 GARCH(-1) 0.330820 0.468976 0.705409 0.4806 R-squared 0.013623 Mean dependent var 0.028048 Adjusted R-squared -0.062252 S.D. dependent var 0.102021 S.E. of regression 0.105149 Akaike info criterion -1.674651 Sum squared resid 0.718660 Schwarz criterion -1.483439 Log likelihood 65.45011 Hannan-Quinn criter. -1.598612 F-statistic 0.179549 Durbin-Watson stat 1.854244 Prob(F-statistic) 0.969349

GARCH(2,1)

Dependent Variable: ANEKAINDUSTRI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:31

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Failure to improve Likelihood after 155 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.043940 0.061028 0.719994 0.4715 BIRATE -0.003056 0.007854 -0.389041 0.6972

Variance Equation

C 0.005385 0.007559 0.712431 0.4762 RESID(-1)^2 0.477610 0.208001 2.296192 0.0217 GARCH(-1) -0.127684 0.467547 -0.273094 0.7848 GARCH(-2) 0.167995 0.491067 0.342103 0.7323 R-squared 0.003385 Mean dependent var 0.028048 Adjusted R-squared -0.073278 S.D. dependent var 0.102021 S.E. of regression 0.105693 Akaike info criterion -1.727455 Sum squared resid 0.726120 Schwarz criterion -1.536243 Log likelihood 67.32467 Hannan-Quinn criter. -1.651416 F-statistic 0.044152 Durbin-Watson stat 1.835886 Prob(F-statistic) 0.998816

Model GARCH Saham Sektor Industri Dasar GARCH(1,0)

Dependent Variable: INDUSTRIDASAR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:22

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 23 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.090212 0.074194 1.215893 0.2240 BIRATE -0.009306 0.009280 -1.002905 0.3159

Variance Equation

C 0.002032 0.009082 0.223690 0.8230 GARCH(-1) 0.732421 1.210880 0.604867 0.5453 R-squared 0.019491 Mean dependent var 0.022214 Adjusted R-squared -0.024412 S.D. dependent var 0.087653 S.E. of regression 0.088717 Akaike info criterion -1.958802 Sum squared resid 0.527337 Schwarz criterion -1.831327 Log likelihood 73.53748 Hannan-Quinn criter. -1.908109 F-statistic 0.443963 Durbin-Watson stat 1.773638 Prob(F-statistic) 0.722356

Dependent Variable: INDUSTRIDASAR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:23

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 26 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.022202 0.074498 0.298022 0.7657 BIRATE 0.000968 0.010246 0.094515 0.9247

Variance Equation

C 0.002014 0.002048 0.983382 0.3254 RESID(-1)^2 0.516785 0.247181 2.090710 0.0366 GARCH(-1) 0.274296 0.385052 0.712362 0.4762 R-squared -0.010135 Mean dependent var 0.022214 Adjusted R-squared -0.071355 S.D. dependent var 0.087653 S.E. of regression 0.090727 Akaike info criterion -2.078132 Sum squared resid 0.543270 Schwarz criterion -1.918788 Log likelihood 78.77367 Hannan-Quinn criter. -2.014766 Durbin-Watson stat 1.719314

GARCH(1,2)

Dependent Variable: INDUSTRIDASAR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:24

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 31 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.023218 0.073335 0.316606 0.7515 BIRATE 0.000741 0.010111 0.073249 0.9416

C 0.001209 0.002102 0.575056 0.5653 RESID(-1)^2 0.463959 0.243495 1.905416 0.0567 RESID(-2)^2 -0.146170 0.502475 -0.290900 0.7711 GARCH(-1) 0.548641 0.687880 0.797583 0.4251 R-squared -0.008133 Mean dependent var 0.022214 Adjusted R-squared -0.085682 S.D. dependent var 0.087653 S.E. of regression 0.091332 Akaike info criterion -2.051361 Sum squared resid 0.542194 Schwarz criterion -1.860149 Log likelihood 78.82332 Hannan-Quinn criter. -1.975322 Durbin-Watson stat 1.722769

GARCH(2,1)

Dependent Variable: INDUSTRIDASAR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:25

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 31 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.022197 0.075061 0.295719 0.7674 BIRATE 0.000972 0.010370 0.093746 0.9253

Variance Equation

C 0.002032 0.002054 0.989526 0.3224 RESID(-1)^2 0.519541 0.251624 2.064756 0.0389 GARCH(-1) 0.274882 0.539695 0.509328 0.6105 GARCH(-2) -0.004716 0.326579 -0.014442 0.9885 R-squared -0.010189 Mean dependent var 0.022214 Adjusted R-squared -0.087896 S.D. dependent var 0.087653 S.E. of regression 0.091425 Akaike info criterion -2.049970 Sum squared resid 0.543300 Schwarz criterion -1.858757 Log likelihood 78.77392 Hannan-Quinn criter. -1.973931 Durbin-Watson stat 1.719220

Model GARCH Saham Sektor Infrastruktur GARCH(1,0)

Dependent Variable: INFRASTRUKTUR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/28/14 Time: 01:08

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 42 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(4) + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. GARCH -35.25713 444.0695 -0.079396 0.9367 C 0.258533 2.092210 0.123569 0.9017 BIRATE -0.012276 0.009548 -1.285717 0.1985

Variance Equation

C 0.000617 0.002162 0.285511 0.7753 GARCH(-1) 0.869431 0.457924 1.898634 0.0576 R-squared 0.043498 Mean dependent var 0.005908 Adjusted R-squared -0.014472 S.D. dependent var 0.067316 S.E. of regression 0.067801 Akaike info criterion -2.472325 Sum squared resid 0.303399 Schwarz criterion -2.312981 Log likelihood 92.76753 Hannan-Quinn criter. -2.408959 F-statistic 0.750354 Durbin-Watson stat 1.888190 Prob(F-statistic) 0.561321

GARCH(1,1)

Dependent Variable: INFRASTRUKTUR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:36

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 88 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.064273 0.049601 1.295802 0.1950 BIRATE -0.008480 0.006993 -1.212645 0.2253

Variance Equation

C 0.000442 0.000546 0.808780 0.4186 RESID(-1)^2 0.164320 0.094097 1.746282 0.0808 GARCH(-1) 0.739395 0.169941 4.350882 0.0000 R-squared 0.026184 Mean dependent var 0.005908

Adjusted R-squared -0.032835 S.D. dependent var 0.067316 S.E. of regression 0.068412 Akaike info criterion -2.548361 Sum squared resid 0.308891 Schwarz criterion -2.389017 Log likelihood 95.46680 Hannan-Quinn criter. -2.484995 F-statistic 0.443653 Durbin-Watson stat 1.854585 Prob(F-statistic) 0.776605

GARCH(1,2)

Dependent Variable: INFRASTRUKTUR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:37

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 33 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.116569 0.050381 2.313735 0.0207 BIRATE -0.015026 0.006127 -2.452488 0.0142

Variance Equation

C 0.009010 0.001106 8.142774 0.0000 RESID(-1)^2 0.060410 0.063636 0.949306 0.3425 RESID(-2)^2 0.010051 0.077979 0.128890 0.8974 GARCH(-1) -1.074862 0.057262 -18.77084 0.0000 R-squared 0.018215 Mean dependent var 0.005908 Adjusted R-squared -0.057307 S.D. dependent var 0.067316 S.E. of regression 0.069218 Akaike info criterion -2.645853 Sum squared resid 0.311419 Schwarz criterion -2.454641 Log likelihood 99.92780 Hannan-Quinn criter. -2.569814 F-statistic 0.241184 Durbin-Watson stat 1.837931 Prob(F-statistic) 0.942734

GARCH(2,1)

Dependent Variable: INFRASTRUKTUR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:40

Included observations: 71 after adjustments Convergence achieved after 62 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.049084 0.047286 1.038031 0.2993 BIRATE -0.006192 0.006647 -0.931458 0.3516

Variance Equation

C 0.000757 0.000945 0.801017 0.4231 RESID(-1)^2 0.278779 0.102748 2.713219 0.0067 GARCH(-1) -0.021224 0.196874 -0.107807 0.9141 GARCH(-2) 0.574198 0.253872 2.261760 0.0237 R-squared 0.023959 Mean dependent var 0.005908 Adjusted R-squared -0.051121 S.D. dependent var 0.067316 S.E. of regression 0.069015 Akaike info criterion -2.558217 Sum squared resid 0.309597 Schwarz criterion -2.367005 Log likelihood 96.81670 Hannan-Quinn criter. -2.482178 F-statistic 0.319117 Durbin-Watson stat 1.851067 Prob(F-statistic) 0.899758

Model GARCH Saham Sektor Keuangan GARCH(1,0)

Dependent Variable: KEUANGAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:41

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 17 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.078259 0.065818 1.189022 0.2344 BIRATE -0.008421 0.008244 -1.021523 0.3070

Variance Equation

C 0.001046 0.005479 0.190950 0.8486 GARCH(-1) 0.830321 0.910705 0.911734 0.3619

R-squared 0.016922 Mean dependent var 0.017305 Adjusted R-squared -0.027097 S.D. dependent var 0.078511 S.E. of regression 0.079568 Akaike info criterion -2.176020 Sum squared resid 0.424179 Schwarz criterion -2.048545 Log likelihood 81.24871 Hannan-Quinn criter. -2.125327 F-statistic 0.384424 Durbin-Watson stat 1.854286 Prob(F-statistic) 0.764555

GARCH(1,1)

Dependent Variable: KEUANGAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:41

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 35 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.011855 0.055148 0.214969 0.8298 BIRATE 0.000979 0.007677 0.127540 0.8985

Variance Equation

C 0.001863 0.001928 0.966656 0.3337 RESID(-1)^2 0.369742 0.188938 1.956952 0.0504 GARCH(-1) 0.335868 0.487351 0.689172 0.4907 R-squared -0.004375 Mean dependent var 0.017305 Adjusted R-squared -0.065246 S.D. dependent var 0.078511 S.E. of regression 0.081032 Akaike info criterion -2.251892 Sum squared resid 0.433368 Schwarz criterion -2.092548 Log likelihood 84.94216 Hannan-Quinn criter. -2.188526 Durbin-Watson stat 1.816130

GARCH(1,2)

Dependent Variable: KEUANGAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:42

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 58 iterations

Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.011359 0.055404 0.205021 0.8376 BIRATE 0.001057 0.007724 0.136798 0.8912

Variance Equation

C 0.001954 0.004678 0.417695 0.6762 RESID(-1)^2 0.372164 0.202493 1.837906 0.0661 RESID(-2)^2 0.016976 0.724542 0.023430 0.9813 GARCH(-1) 0.303108 1.498410 0.202287 0.8397 R-squared -0.004739 Mean dependent var 0.017305 Adjusted R-squared -0.082026 S.D. dependent var 0.078511 S.E. of regression 0.081668 Akaike info criterion -2.223761 Sum squared resid 0.433525 Schwarz criterion -2.032549 Log likelihood 84.94352 Hannan-Quinn criter. -2.147722 Durbin-Watson stat 1.815486

GARCH(2,1)

Dependent Variable: KEUANGAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:42

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 37 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.011384 0.055387 0.205538 0.8372 BIRATE 0.001053 0.007722 0.136369 0.8915

Variance Equation

C 0.001870 0.001975 0.946861 0.3437 RESID(-1)^2 0.372579 0.199310 1.869347 0.0616 GARCH(-1) 0.355180 0.742001 0.478678 0.6322 GARCH(-2) -0.021998 0.640606 -0.034340 0.9726

R-squared -0.004723 Mean dependent var 0.017305 Adjusted R-squared -0.082010 S.D. dependent var 0.078511 S.E. of regression 0.081667 Akaike info criterion -2.223766 Sum squared resid 0.433518 Schwarz criterion -2.032554 Log likelihood 84.94370 Hannan-Quinn criter. -2.147727 Durbin-Watson stat 1.815513

Model GARCH Saham Sektor Konsumsi GARCH(1,0)

Dependent Variable: KONSUMSI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:43

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 20 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.105356 0.052859 1.993148 0.0462 BIRATE -0.011585 0.007221 -1.604393 0.1086

Variance Equation

C 0.000326 0.000405 0.806309 0.4201 GARCH(-1) 0.907008 0.130775 6.935614 0.0000 R-squared 0.057707 Mean dependent var 0.021403 Adjusted R-squared 0.015515 S.D. dependent var 0.058264 S.E. of regression 0.057810 Akaike info criterion -2.850576 Sum squared resid 0.223912 Schwarz criterion -2.723101 Log likelihood 105.1955 Hannan-Quinn criter. -2.799884 F-statistic 1.367719 Durbin-Watson stat 1.641337 Prob(F-statistic) 0.260192

GARCH(1,1)

Dependent Variable: KONSUMSI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:44

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 23 iterations

Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.103879 0.050229 2.068106 0.0386 BIRATE -0.012011 0.006939 -1.731050 0.0834

Variance Equation

C 0.001337 0.001623 0.824190 0.4098 RESID(-1)^2 0.166347 0.141323 1.177067 0.2392 GARCH(-1) 0.412978 0.603527 0.684274 0.4938 R-squared 0.053782 Mean dependent var 0.021403 Adjusted R-squared -0.003564 S.D. dependent var 0.058264 S.E. of regression 0.058367 Akaike info criterion -2.838256 Sum squared resid 0.224845 Schwarz criterion -2.678913 Log likelihood 105.7581 Hannan-Quinn criter. -2.774891 F-statistic 0.937846 Durbin-Watson stat 1.634441 Prob(F-statistic) 0.447652

GARCH(1,2)

Dependent Variable: KONSUMSI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:44

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 26 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.094314 0.052448 1.798248 0.0721 BIRATE -0.010539 0.007264 -1.450889 0.1468

Variance Equation

C 0.000258 0.000368 0.702654 0.4823 RESID(-1)^2 0.204207 0.165867 1.231150 0.2183 RESID(-2)^2 -0.181715 0.171298 -1.060813 0.2888 GARCH(-1) 0.905860 0.173493 5.221318 0.0000 R-squared 0.054729 Mean dependent var 0.021403

Adjusted R-squared -0.017984 S.D. dependent var 0.058264 S.E. of regression 0.058785 Akaike info criterion -2.823170 Sum squared resid 0.224620 Schwarz criterion -2.631957 Log likelihood 106.2225 Hannan-Quinn criter. -2.747131 F-statistic 0.752673 Durbin-Watson stat 1.636399 Prob(F-statistic) 0.587165

GARCH(2,1)

Dependent Variable: KONSUMSI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:45

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 158 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.096919 0.038718 2.503209 0.0123 BIRATE -0.009711 0.005110 -1.900383 0.0574

Variance Equation

C 0.002528 0.000480 5.267988 0.0000 RESID(-1)^2 0.086248 0.068092 1.266635 0.2053 GARCH(-1) 1.007144 0.120773 8.339150 0.0000 GARCH(-2) -0.910742 0.076363 -11.92650 0.0000 R-squared 0.045630 Mean dependent var 0.021403 Adjusted R-squared -0.027784 S.D. dependent var 0.058264 S.E. of regression 0.059067 Akaike info criterion -2.875107 Sum squared resid 0.226782 Schwarz criterion -2.683895 Log likelihood 108.0663 Hannan-Quinn criter. -2.799068 F-statistic 0.621546 Durbin-Watson stat 1.620994 Prob(F-statistic) 0.683819

Model GARCH Saham Sektor Manufaktur GARCH(1,0)

Dependent Variable: MANUFAKTUR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:46

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 20 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.098637 0.070551 1.398107 0.1621 BIRATE -0.010414 0.009219 -1.129701 0.2586

Variance Equation

C 0.001027 0.003538 0.290250 0.7716 GARCH(-1) 0.802071 0.701758 1.142946 0.2531 R-squared 0.033814 Mean dependent var 0.022731 Adjusted R-squared -0.009448 S.D. dependent var 0.072436 S.E. of regression 0.072777 Akaike info criterion -2.362966 Sum squared resid 0.354869 Schwarz criterion -2.235491 Log likelihood 87.88531 Hannan-Quinn criter. -2.312274 F-statistic 0.781605 Durbin-Watson stat 1.629380 Prob(F-statistic) 0.508365

GARCH(1,1)

Dependent Variable: MANUFAKTUR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:47

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 41 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.045745 0.059722 0.765963 0.4437 BIRATE -0.002768 0.008336 -0.332010 0.7399

Variance Equation

C 0.002264 0.001304 1.736358 0.0825 RESID(-1)^2 0.533794 0.210543 2.535325 0.0112 GARCH(-1) 0.100982 0.246956 0.408905 0.6826 R-squared 0.012523 Mean dependent var 0.022731

Adjusted R-squared -0.047324 S.D. dependent var 0.072436 S.E. of regression 0.074130 Akaike info criterion -2.436287 Sum squared resid 0.362689 Schwarz criterion -2.276943 Log likelihood 91.48819 Hannan-Quinn criter. -2.372921 F-statistic 0.209244 Durbin-Watson stat 1.594501 Prob(F-statistic) 0.932425

GARCH(1,2)

Dependent Variable: MANUFAKTUR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:48

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 43 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.044711 0.054820 0.815601 0.4147 BIRATE -0.002565 0.007534 -0.340393 0.7336

Variance Equation

C 0.000721 0.002633 0.273999 0.7841 RESID(-1)^2 0.538192 0.219900 2.447440 0.0144 RESID(-2)^2 -0.428714 0.512920 -0.835831 0.4033 GARCH(-1) 0.774025 0.876579 0.883007 0.3772 R-squared 0.011048 Mean dependent var 0.022731 Adjusted R-squared -0.065025 S.D. dependent var 0.072436 S.E. of regression 0.074754 Akaike info criterion -2.413964 Sum squared resid 0.363230 Schwarz criterion -2.222751 Log likelihood 91.69571 Hannan-Quinn criter. -2.337925 F-statistic 0.145235 Durbin-Watson stat 1.592140 Prob(F-statistic) 0.980780

GARCH(2,1)

Dependent Variable: MANUFAKTUR

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:48

Included observations: 71 after adjustments Failure to improve Likelihood after 27 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.053366 0.051474 1.036748 0.2999 BIRATE -0.003726 0.007225 -0.515752 0.6060

Variance Equation

C 0.002174 0.001263 1.720645 0.0853 RESID(-1)^2 0.590881 0.234154 2.523470 0.0116 GARCH(-1) 0.146032 0.253577 0.575887 0.5647 GARCH(-2) -0.080782 0.230755 -0.350075 0.7263 R-squared 0.015612 Mean dependent var 0.022731 Adjusted R-squared -0.060110 S.D. dependent var 0.072436 S.E. of regression 0.074581 Akaike info criterion -2.447420 Sum squared resid 0.361554 Schwarz criterion -2.256208 Log likelihood 92.88342 Hannan-Quinn criter. -2.371381 F-statistic 0.206179 Durbin-Watson stat 1.599434 Prob(F-statistic) 0.958756

Model GARCH Saham Sektor Perdagangan dan Jasa GARCH(1,0)

Dependent Variable: PERDAGANGANJASA

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:49

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 43 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.149097 0.065447 2.278150 0.0227 BIRATE -0.017768 0.008519 -2.085747 0.0370

Variance Equation

GARCH(-1) -0.986072 0.058233 -16.93335 0.0000 R-squared 0.087464 Mean dependent var 0.017332 Adjusted R-squared 0.046604 S.D. dependent var 0.085297 S.E. of regression 0.083286 Akaike info criterion -2.103147 Sum squared resid 0.464744 Schwarz criterion -1.975673 Log likelihood 78.66174 Hannan-Quinn criter. -2.052455 F-statistic 2.140583 Durbin-Watson stat 1.408596 Prob(F-statistic) 0.103288

GARCH(1,1)

Dependent Variable: PERDAGANGANJASA

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:50

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 22 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.171690 0.008468 20.27462 0.0000 BIRATE -0.021631 0.001974 -10.95932 0.0000

Variance Equation

C -0.000118 0.000235 -0.501487 0.6160 RESID(-1)^2 -0.026201 0.014661 -1.787094 0.0739 GARCH(-1) 1.046413 0.042259 24.76178 0.0000 R-squared 0.094023 Mean dependent var 0.017332 Adjusted R-squared 0.039115 S.D. dependent var 0.085297 S.E. of regression 0.083612 Akaike info criterion -2.181371 Sum squared resid 0.461404 Schwarz criterion -2.022027 Log likelihood 82.43866 Hannan-Quinn criter. -2.118005 F-statistic 1.712376 Durbin-Watson stat 1.417300 Prob(F-statistic) 0.157742

GARCH(1,2)

Dependent Variable: PERDAGANGANJASA

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:51

Included observations: 71 after adjustments Convergence achieved after 58 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.079318 0.052587 1.508304 0.1315 BIRATE -0.007422 0.006967 -1.065335 0.2867

Variance Equation

C 0.002043 0.003009 0.679048 0.4971 RESID(-1)^2 0.414842 0.138834 2.988039 0.0028 RESID(-2)^2 -0.279965 0.157093 -1.782160 0.0747 GARCH(-1) 0.550499 0.679170 0.810548 0.4176 R-squared 0.041104 Mean dependent var 0.017332 Adjusted R-squared -0.032657 S.D. dependent var 0.085297 S.E. of regression 0.086678 Akaike info criterion -2.146337 Sum squared resid 0.488355 Schwarz criterion -1.955124 Log likelihood 82.19495 Hannan-Quinn criter. -2.070297 F-statistic 0.557258 Durbin-Watson stat 1.344950 Prob(F-statistic) 0.732259

GARCH(2,1)

Dependent Variable: PERDAGANGANJASA

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:52

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 90 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.061677 0.063925 0.964840 0.3346 BIRATE -0.004329 0.009065 -0.477612 0.6329

Variance Equation

C 0.004628 0.001848 2.504129 0.0123 RESID(-1)^2 0.719556 0.205498 3.501518 0.0005

GARCH(-1) -0.146196 0.157033 -0.930989 0.3519 GARCH(-2) -0.043244 0.088116 -0.490770 0.6236 R-squared 0.008062 Mean dependent var 0.017332 Adjusted R-squared -0.068241 S.D. dependent var 0.085297 S.E. of regression 0.088159 Akaike info criterion -2.149011 Sum squared resid 0.505183 Schwarz criterion -1.957798 Log likelihood 82.28988 Hannan-Quinn criter. -2.072972 F-statistic 0.105656 Durbin-Watson stat 1.301622 Prob(F-statistic) 0.990659

Model GARCH Saham Sektor Pertambangan GARCH(1,0)

Dependent Variable: PERTAMBANGAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:53

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 24 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.023589 0.118942 0.198322 0.8428 BIRATE -0.000887 0.014716 -0.060258 0.9520

Variance Equation

C 0.009664 2.115839 0.004567 0.9964 GARCH(-1) 0.411252 128.8906 0.003191 0.9975 R-squared 0.000052 Mean dependent var 0.017278 Adjusted R-squared -0.044722 S.D. dependent var 0.129067 S.E. of regression 0.131922 Akaike info criterion -1.158531 Sum squared resid 1.166027 Schwarz criterion -1.031056 Log likelihood 45.12784 Hannan-Quinn criter. -1.107838 F-statistic 0.001152 Durbin-Watson stat 1.112704 Prob(F-statistic) 0.999945

GARCH(1,1)

Dependent Variable: PERTAMBANGAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:54

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 24 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C -0.218819 0.086093 -2.541652 0.0110 BIRATE 0.034990 0.011577 3.022320 0.0025

Variance Equation

C 0.001716 0.001028 1.669011 0.0951 RESID(-1)^2 0.260031 0.110375 2.355876 0.0185 GARCH(-1) 0.648240 0.091252 7.103873 0.0000 R-squared -0.116503 Mean dependent var 0.017278 Adjusted R-squared -0.184170 S.D. dependent var 0.129067 S.E. of regression 0.140451 Akaike info criterion -1.344440 Sum squared resid 1.301940 Schwarz criterion -1.185096 Log likelihood 52.72762 Hannan-Quinn criter. -1.281074 Durbin-Watson stat 1.003615

GARCH(1,2)

Dependent Variable: PERTAMBANGAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:54

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 189 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C -0.081147 0.118451 -0.685069 0.4933 BIRATE 0.014682 0.015690 0.935739 0.3494

Variance Equation

C 0.013047 0.004593 2.840449 0.0045 RESID(-1)^2 0.371995 0.155923 2.385762 0.0170 RESID(-2)^2 -0.076895 0.135313 -0.568272 0.5699 GARCH(-1) -0.165655 0.372474 -0.444743 0.6565

R-squared -0.022135 Mean dependent var 0.017278 Adjusted R-squared -0.100761 S.D. dependent var 0.129067 S.E. of regression 0.135414 Akaike info criterion -1.318260 Sum squared resid 1.191899 Schwarz criterion -1.127048 Log likelihood 52.79823 Hannan-Quinn criter. -1.242221 Durbin-Watson stat 1.091305

GARCH(2,1)

Dependent Variable: PERTAMBANGAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:55

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 33 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C -0.197053 0.084374 -2.335465 0.0195 BIRATE 0.031597 0.011323 2.790462 0.0053

Variance Equation

C 0.002204 0.001414 1.557988 0.1192 RESID(-1)^2 0.357081 0.169157 2.110947 0.0348 GARCH(-1) 0.155977 0.396573 0.393312 0.6941 GARCH(-2) 0.364059 0.318996 1.141264 0.2538 R-squared -0.093601 Mean dependent var 0.017278 Adjusted R-squared -0.177724 S.D. dependent var 0.129067 S.E. of regression 0.140068 Akaike info criterion -1.339221 Sum squared resid 1.275234 Schwarz criterion -1.148009 Log likelihood 53.54235 Hannan-Quinn criter. -1.263182 Durbin-Watson stat 1.023756

Model GARCH Saham Sektor Pertanian GARCH(1,0)

Dependent Variable: PERTANIAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:56

Included observations: 71 after adjustments Convergence achieved after 39 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.057643 0.067182 0.858013 0.3909 BIRATE -0.008082 0.009467 -0.853694 0.3933

Variance Equation

C -9.96E-05 0.000233 -0.426661 0.6696 GARCH(-1) 0.982229 0.023227 42.28770 0.0000 R-squared -0.020071 Mean dependent var 0.015702 Adjusted R-squared -0.065746 S.D. dependent var 0.118530 S.E. of regression 0.122364 Akaike info criterion -1.571328 Sum squared resid 1.003195 Schwarz criterion -1.443853 Log likelihood 59.78214 Hannan-Quinn criter. -1.520635 Durbin-Watson stat 1.398215

GARCH(1,1)

Dependent Variable: PERTANIAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:56

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 83 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C -0.024396 0.070427 -0.346394 0.7290 BIRATE 0.005032 0.010158 0.495354 0.6204

Variance Equation

C 0.000282 0.000684 0.412959 0.6796 RESID(-1)^2 0.139563 0.043094 3.238601 0.0012 GARCH(-1) 0.824634 0.066698 12.36365 0.0000 R-squared -0.006534 Mean dependent var 0.015702 Adjusted R-squared -0.067536 S.D. dependent var 0.118530 S.E. of regression 0.122467 Akaike info criterion -1.512885

Sum squared resid 0.989882 Schwarz criterion -1.353542 Log likelihood 58.70743 Hannan-Quinn criter. -1.449519 Durbin-Watson stat 1.417195

GARCH(1,2)

Dependent Variable: PERTANIAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:57

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 49 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C -0.188451 0.072548 -2.597591 0.0094 BIRATE 0.031643 0.010651 2.970794 0.0030

Variance Equation

C -9.19E-05 0.000129 -0.711468 0.4768 RESID(-1)^2 0.261799 0.144102 1.816764 0.0693 RESID(-2)^2 -0.323150 0.142185 -2.272742 0.0230 GARCH(-1) 1.078974 0.038011 28.38604 0.0000 R-squared -0.152203 Mean dependent var 0.015702 Adjusted R-squared -0.240834 S.D. dependent var 0.118530 S.E. of regression 0.132034 Akaike info criterion -1.742808 Sum squared resid 1.133141 Schwarz criterion -1.551596 Log likelihood 67.86970 Hannan-Quinn criter. -1.666769 Durbin-Watson stat 1.240434

GARCH(2,1)

Dependent Variable: PERTANIAN

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:57

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Failure to improve Likelihood after 15 iterations Presample variance: backcast (parameter = 0.7)

C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C -0.026939 0.084516 -0.318744 0.7499 BIRATE 0.008149 0.010164 0.801767 0.4227

Variance Equation

C 0.008175 0.004440 1.841182 0.0656 RESID(-1)^2 0.373547 0.244909 1.525248 0.1272 GARCH(-1) 0.149468 0.421067 0.354974 0.7226 GARCH(-2) -0.120768 0.185671 -0.650442 0.5154 R-squared -0.029324 Mean dependent var 0.015702 Adjusted R-squared -0.108503 S.D. dependent var 0.118530 S.E. of regression 0.124795 Akaike info criterion -1.431487 Sum squared resid 1.012295 Schwarz criterion -1.240275 Log likelihood 56.81779 Hannan-Quinn criter. -1.355448 Durbin-Watson stat 1.385971

Model GARCH Saham Sektor Properti GARCH(1,0)

Dependent Variable: PROPERTI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:58

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 26 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.121944 0.064826 1.881098 0.0600 BIRATE -0.015168 0.009366 -1.619424 0.1054

Variance Equation

C 0.000165 0.000329 0.501835 0.6158 GARCH(-1) 0.954675 0.047100 20.26893 0.0000 R-squared 0.013882 Mean dependent var 0.017867 Adjusted R-squared -0.030272 S.D. dependent var 0.088924 S.E. of regression 0.090260 Akaike info criterion -2.013533 Sum squared resid 0.545836 Schwarz criterion -1.886058

Log likelihood 75.48042 Hannan-Quinn criter. -1.962840 F-statistic 0.314399 Durbin-Watson stat 1.638915 Prob(F-statistic) 0.814905

GARCH(1,1)

Dependent Variable: PROPERTI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:59

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 14 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.124940 0.010367 12.05207 0.0000 BIRATE -0.014893 0.000396 -37.63128 0.0000

Variance Equation

C 0.000307 0.000233 1.319773 0.1869 RESID(-1)^2 -0.115905 0.087195 -1.329260 0.1838 GARCH(-1) 1.064423 0.088546 12.02115 0.0000 R-squared 0.017251 Mean dependent var 0.017867 Adjusted R-squared -0.042309 S.D. dependent var 0.088924 S.E. of regression 0.090785 Akaike info criterion -2.107694 Sum squared resid 0.543971 Schwarz criterion -1.948350 Log likelihood 79.82314 Hannan-Quinn criter. -2.044328 F-statistic 0.289646 Durbin-Watson stat 1.644535 Prob(F-statistic) 0.883700

GARCH(1,2)

Dependent Variable: PROPERTI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 22:59

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 22 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-2)^2 + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob. C 0.119090 0.003203 37.17491 0.0000 BIRATE -0.014128 0.000708 -19.94579 0.0000

Variance Equation

C 0.000345 0.000277 1.244573 0.2133 RESID(-1)^2 -0.018072 0.215778 -0.083751 0.9333 RESID(-2)^2 -0.122960 0.192280 -0.639480 0.5225 GARCH(-1) 1.093254 0.099954 10.93756 0.0000 R-squared 0.018185 Mean dependent var 0.017867 Adjusted R-squared -0.057339 S.D. dependent var 0.088924 S.E. of regression 0.091438 Akaike info criterion -2.080818 Sum squared resid 0.543454 Schwarz criterion -1.889605 Log likelihood 79.86903 Hannan-Quinn criter. -2.004779 F-statistic 0.240788 Durbin-Watson stat 1.646105 Prob(F-statistic) 0.942928

GARCH(2,1)

Dependent Variable: PROPERTI

Method: ML - ARCH (Marquardt) - Normal distribution Date: 04/07/14 Time: 23:00

Sample (adjusted): 2007M02 2012M12 Included observations: 71 after adjustments Convergence achieved after 28 iterations Presample variance: backcast (parameter = 0.7)

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) + C(6)*GARCH(-2)

Variable Coefficient Std. Error z-Statistic Prob. C 0.125411 0.012744 9.841020 0.0000 BIRATE -0.015607 0.000286 -54.64823 0.0000

Variance Equation

C 0.000425 0.000376 1.131334 0.2579 RESID(-1)^2 -0.185614 0.073657 -2.519988 0.0117 GARCH(-1) 0.200037 0.119977 1.667296 0.0955 GARCH(-2) 0.894156 0.134962 6.625255 0.0000 R-squared 0.013596 Mean dependent var 0.017867 Adjusted R-squared -0.062281 S.D. dependent var 0.088924 S.E. of regression 0.091651 Akaike info criterion -2.048827

Sum squared resid 0.545994 Schwarz criterion -1.857615 Log likelihood 78.73336 Hannan-Quinn criter. -1.972788 F-statistic 0.179188 Durbin-Watson stat 1.638439 Prob(F-statistic) 0.969482

Lampiran 2

Indeks Harga Saham Gabungan

Aneka Industri Industri Dasar 0 2 4 6 8 10 12 14

-2 -1 0 1 2

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean -0.011997 Median 0.072551 Maximum 2.278930 Minimum -2.406038 Std. Dev. 1.006978 Skewness -0.189870 Kurtosis 3.076828

Jarque-Bera 0.444059 Probability 0.800892

0 2 4 6 8 10 12

-2 -1 0 1 2

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean -0.001741 Median 0.140747 Maximum 2.146772 Minimum -2.679126 Std. Dev. 1.007216 Skewness -0.227966 Kurtosis 2.609973

Jarque-Bera 1.064987 Probability 0.587139

0 2 4 6 8 10

-2 -1 0 1 2

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean -0.073750 Median -0.001169 Maximum 2.059294 Minimum -2.418695 Std. Dev. 1.002866 Skewness -0.105991 Kurtosis 2.719182

Jarque-Bera 0.366226 Probability 0.832674

Infrastruktur Keuangan Konsumsi 0 2 4 6 8 10 12

-3 -2 -1 0 1 2 3 4

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean 0.530321 Median 0.239384 Maximum 4.109921 Minimum -3.088321 Std. Dev. 1.313170 Skewness 0.611019 Kurtosis 3.718853

Jarque-Bera 5.946626 Probability 0.051134

0 2 4 6 8 10 12

-3 -2 -1 0 1 2

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean -0.051576 Median -0.004718 Maximum 2.029503 Minimum -2.974468 Std. Dev. 1.006072 Skewness -0.166827 Kurtosis 2.990730

Jarque-Bera 0.329589 Probability 0.848068

0 2 4 6 8 10 12

-3 -2 -1 0 1 2 3

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean 0.051409 Median 0.058904 Maximum 2.860451 Minimum -2.917503 Std. Dev. 1.003578 Skewness -0.057994 Kurtosis 3.928882

Jarque-Bera 2.592311 Probability 0.273582

Manufaktur

Perdagangan dan Jasa

Pertambangan 0 2 4 6 8 10 12 14

-2 -1 0 1 2

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean -0.051923 Median 0.026307 Maximum 2.698101 Minimum -2.574340 Std. Dev. 1.005376 Skewness -0.106450 Kurtosis 3.177776

Jarque-Bera 0.227587 Probability 0.892442

0 2 4 6 8 10 12

-4 -3 -2 -1 0 1 2

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean 0.009734 Median 0.130153 Maximum 2.242061 Minimum -3.906740 Std. Dev. 1.110530 Skewness -0.667599 Kurtosis 4.059943

Jarque-Bera 8.597596 Probability 0.013585

0 2 4 6 8 10 12 14

-2 -1 0 1 2

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean -0.081570 Median -0.019148 Maximum 2.624542 Minimum -2.291648 Std. Dev. 0.999946 Skewness -0.247521 Kurtosis 3.504903

Jarque-Bera 1.479148 Probability 0.477317

Pertanian

Properti

0 2 4 6 8 10 12 14

-3 -2 -1 0 1 2 3

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean -0.107074 Median -0.101303 Maximum 2.750506 Minimum -2.966283 Std. Dev. 1.079529 Skewness -0.083920 Kurtosis 3.262141

Jarque-Bera 0.286626 Probability 0.866483

0 1 2 3 4 5 6 7 8 9

-2 -1 0 1 2

Series: Standardized Residuals Sample 2007M02 2012M12 Observations 71

Mean -0.012877 Median -0.011270 Maximum 1.913197 Minimum -2.476446 Std. Dev. 1.057423 Skewness -0.305917 Kurtosis 2.471788

Jarque-Bera 1.932824 Probability 0.380446

Lampiran 3 ARCH LM Test IHSG

Heteroskedasticity Test: ARCH

F-statistic 0.001799 Prob. F(1,68) 0.9663 Obs*R-squared 0.001852 Prob. Chi-Square(1) 0.9657

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/07/14 Time: 22:19

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.005898 0.214770 4.683600 0.0000 WGT_RESID^2(-1) 0.005150 0.121412 0.042416 0.9663 R-squared 0.000026 Mean dependent var 1.011120 Adjusted R-squared -0.014679 S.D. dependent var 1.461739 S.E. of regression 1.472428 Akaike info criterion 3.639858 Sum squared resid 147.4271 Schwarz criterion 3.704101 Log likelihood -125.3950 Hannan-Quinn criter. 3.665376 F-statistic 0.001799 Durbin-Watson stat 1.997031 Prob(F-statistic) 0.966292

Aneka Industri

Heteroskedasticity Test: ARCH

F-statistic 0.041474 Prob. F(1,68) 0.8392 Obs*R-squared 0.042668 Prob. Chi-Square(1) 0.8364

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:39

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.037650 0.197512 5.253601 0.0000 WGT_RESID^2(-1) -0.024710 0.121333 -0.203652 0.8392 R-squared 0.000610 Mean dependent var 1.012593 Adjusted R-squared -0.014087 S.D. dependent var 1.283694 S.E. of regression 1.292704 Akaike info criterion 3.379505 Sum squared resid 113.6338 Schwarz criterion 3.443748 Log likelihood -116.2827 Hannan-Quinn criter. 3.405023 F-statistic 0.041474 Durbin-Watson stat 1.989656 Prob(F-statistic) 0.839233

Industri Dasar

Heteroskedasticity Test: ARCH

F-statistic 0.021552 Prob. F(1,68) 0.8837 Obs*R-squared 0.022179 Prob. Chi-Square(1) 0.8816

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:40

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.029226 0.202017 5.094759 0.0000 WGT_RESID^2(-1) -0.017800 0.121251 -0.146806 0.8837 R-squared 0.000317 Mean dependent var 1.011225 Adjusted R-squared -0.014384 S.D. dependent var 1.333697 S.E. of regression 1.343255 Akaike info criterion 3.456223 Sum squared resid 122.6946 Schwarz criterion 3.520466 Log likelihood -118.9678 Hannan-Quinn criter. 3.481741 F-statistic 0.021552 Durbin-Watson stat 1.993179 Prob(F-statistic) 0.883719

Infrastruktur

Heteroskedasticity Test: ARCH

Obs*R-squared 1.043063 Prob. Chi-Square(1) 0.3071

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:42

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.010927 0.297173 3.401808 0.0011 WGT_RESID^2(-1) 0.122009 0.120302 1.014193 0.3141 R-squared 0.014901 Mean dependent var 1.151075 Adjusted R-squared 0.000414 S.D. dependent var 2.201631 S.E. of regression 2.201175 Akaike info criterion 4.444015 Sum squared resid 329.4716 Schwarz criterion 4.508258 Log likelihood -153.5405 Hannan-Quinn criter. 4.469533 F-statistic 1.028588 Durbin-Watson stat 1.991459 Prob(F-statistic) 0.314086

Keuangan

Heteroskedasticity Test: ARCH

F-statistic 0.061234 Prob. F(1,68) 0.8053 Obs*R-squared 0.062978 Prob. Chi-Square(1) 0.8018

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:43

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.025101 0.212998 4.812730 0.0000 WGT_RESID^2(-1) -0.030073 0.121528 -0.247455 0.8053 R-squared 0.000900 Mean dependent var 0.994601 Adjusted R-squared -0.013793 S.D. dependent var 1.443465 S.E. of regression 1.453386 Akaike info criterion 3.613824 Sum squared resid 143.6385 Schwarz criterion 3.678067

Log likelihood -124.4839 Hannan-Quinn criter. 3.639342 F-statistic 0.061234 Durbin-Watson stat 1.984327 Prob(F-statistic) 0.805302

Konsumsi

Heteroskedasticity Test: ARCH

F-statistic 1.241679 Prob. F(1,68) 0.2691 Obs*R-squared 1.255277 Prob. Chi-Square(1) 0.2625

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:44

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 0.864463 0.232532 3.717606 0.0004 WGT_RESID^2(-1) 0.134386 0.120601 1.114306 0.2691 R-squared 0.017933 Mean dependent var 0.993495 Adjusted R-squared 0.003490 S.D. dependent var 1.690076 S.E. of regression 1.687124 Akaike info criterion 3.912083 Sum squared resid 193.5544 Schwarz criterion 3.976326 Log likelihood -134.9229 Hannan-Quinn criter. 3.937601 F-statistic 1.241679 Durbin-Watson stat 2.043941 Prob(F-statistic) 0.269071

Manufaktur

Heteroskedasticity Test: ARCH

F-statistic 0.062467 Prob. F(1,68) 0.8034 Obs*R-squared 0.064245 Prob. Chi-Square(1) 0.7999

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:44

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.042509 0.217792 4.786709 0.0000 WGT_RESID^2(-1) -0.030253 0.121044 -0.249933 0.8034 R-squared 0.000918 Mean dependent var 1.012015 Adjusted R-squared -0.013775 S.D. dependent var 1.499123 S.E. of regression 1.509412 Akaike info criterion 3.689473 Sum squared resid 154.9262 Schwarz criterion 3.753716 Log likelihood -127.1316 Hannan-Quinn criter. 3.714991 F-statistic 0.062467 Durbin-Watson stat 1.998219 Prob(F-statistic) 0.803392

Perdagangan dan jasa

Heteroskedasticity Test: ARCH

F-statistic 0.041893 Prob. F(1,68) 0.8384 Obs*R-squared 0.043099 Prob. Chi-Square(1) 0.8355

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:45

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.199512 0.297771 4.028308 0.0001 WGT_RESID^2(-1) 0.024786 0.121099 0.204678 0.8384 R-squared 0.000616 Mean dependent var 1.229831 Adjusted R-squared -0.014081 S.D. dependent var 2.146128 S.E. of regression 2.161185 Akaike info criterion 4.407346 Sum squared resid 317.6090 Schwarz criterion 4.471589 Log likelihood -152.2571 Hannan-Quinn criter. 4.432864 F-statistic 0.041893 Durbin-Watson stat 1.997925 Prob(F-statistic) 0.838435

Pertambangan

F-statistic 0.904505 Prob. F(1,68) 0.3449 Obs*R-squared 0.918886 Prob. Chi-Square(1) 0.3378

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:46

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 0.890771 0.227267 3.919493 0.0002 WGT_RESID^2(-1) 0.114386 0.120273 0.951055 0.3449 R-squared 0.013127 Mean dependent var 1.005014 Adjusted R-squared -0.001386 S.D. dependent var 1.613031 S.E. of regression 1.614149 Akaike info criterion 3.823648 Sum squared resid 177.1724 Schwarz criterion 3.887891 Log likelihood -131.8277 Hannan-Quinn criter. 3.849166 F-statistic 0.904505 Durbin-Watson stat 1.987261 Prob(F-statistic) 0.344945

Pertanian

Heteroskedasticity Test: ARCH

F-statistic 0.543819 Prob. F(1,68) 0.4634 Obs*R-squared 0.555372 Prob. Chi-Square(1) 0.4561

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:47

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.272948 0.255137 4.989275 0.0000 WGT_RESID^2(-1) -0.088985 0.120668 -0.737441 0.4634 R-squared 0.007934 Mean dependent var 1.169810 Adjusted R-squared -0.006655 S.D. dependent var 1.779413 S.E. of regression 1.785324 Akaike info criterion 4.025232

Sum squared resid 216.7420 Schwarz criterion 4.089475 Log likelihood -138.8831 Hannan-Quinn criter. 4.050750 F-statistic 0.543819 Durbin-Watson stat 2.023854 Prob(F-statistic) 0.463391

Properti

Heteroskedasticity Test: ARCH

F-statistic 0.151046 Prob. F(1,68) 0.6988 Obs*R-squared 0.155144 Prob. Chi-Square(1) 0.6937

Test Equation:

Dependent Variable: WGT_RESID^2 Method: Least Squares

Date: 04/08/14 Time: 21:48

Sample (adjusted): 2007M03 2012M12 Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C 1.157683 0.211775 5.466574 0.0000 WGT_RESID^2(-1) -0.047081 0.121142 -0.388646 0.6988 R-squared 0.002216 Mean dependent var 1.105604 Adjusted R-squared -0.012457 S.D. dependent var 1.363570 S.E. of regression 1.372037 Akaike info criterion 3.498625 Sum squared resid 128.0090 Schwarz criterion 3.562868 Log likelihood -120.4519 Hannan-Quinn criter. 3.524143 F-statistic 0.151046 Durbin-Watson stat 2.013586 Prob(F-statistic) 0.698752

Lampiran 4

ACF dan PACF untuk lag 1-10 IHSG

Aneka Industri

AC PAC Q-Stat Prob

1 0.012 0.012 0.0108 0.917 2 0.112 0.112 0.9548 0.620 3 0.187 0.186 3.6060 0.307 4 -0.098 -0.116 4.3514 0.361 5 0.000 -0.044 4.3514 0.500 6 0.120 0.118 5.4909 0.483 7 -0.149 -0.114 7.2886 0.399 8 -0.004 -0.038 7.2896 0.506 9 -0.053 -0.068 7.5245 0.583 10 -0.138 -0.067 9.1352 0.519

Industri Dasar

AC PAC Q-Stat Prob

1 0.082 0.082 0.4924 0.483 2 -0.130 -0.138 1.7630 0.414 3 0.163 0.191 3.7775 0.287 4 0.083 0.030 4.3094 0.366 5 -0.079 -0.045 4.7986 0.441 6 -0.001 -0.001 4.7986 0.570 7 -0.091 -0.137 5.4658 0.603 8 0.001 0.048 5.4659 0.707 9 0.011 -0.020 5.4758 0.791

AC PAC Q-Stat Prob

1 0.119 0.119 1.0423 0.307 2 -0.014 -0.029 1.0571 0.589 3 0.278 0.288 6.9627 0.073 4 -0.003 -0.082 6.9634 0.138 5 -0.034 -0.000 7.0530 0.217 6 0.040 -0.044 7.1805 0.304 7 0.048 0.077 7.3644 0.392 8 -0.032 -0.045 7.4501 0.489 9 -0.074 -0.066 7.9062 0.544 10 -0.185 -0.226 10.799 0.373

10 -0.139 -0.109 7.1284 0.713

Infrastruktur

AC PAC Q-Stat Prob

1 0.032 0.032 0.0778 0.780 2 -0.188 -0.190 2.7423 0.254 3 0.163 0.183 4.7634 0.190 4 0.160 0.112 6.7333 0.151 5 -0.103 -0.058 7.5596 0.182 6 -0.023 0.006 7.6024 0.269 7 -0.093 -0.180 8.3068 0.306 8 0.004 0.027 8.3078 0.404 9 -0.027 -0.055 8.3701 0.497 10 -0.126 -0.088 9.7095 0.466

Keuangan

AC PAC Q-Stat Prob

1 0.006 0.006 0.0029 0.957 2 -0.159 -0.159 1.9037 0.386 3 0.194 0.201 4.7773 0.189 4 0.066 0.034 5.1162 0.276 5 -0.019 0.043 5.1449 0.398 6 0.002 -0.024 5.1452 0.525 7 -0.013 -0.030 5.1585 0.641 8 0.002 -0.007 5.1588 0.740 9 0.102 0.104 6.0219 0.738 10 -0.107 -0.112 6.9903 0.726

Konsumsi

AC PAC Q-Stat Prob

1 0.166 0.166 2.0404 0.153 2 0.056 0.030 2.2784 0.320 3 0.051 0.038 2.4733 0.480 4 0.025 0.009 2.5208 0.641 5 0.129 0.124 3.8234 0.575 6 0.009 -0.035 3.8299 0.700 7 -0.001 -0.007 3.8300 0.799 8 -0.078 -0.089 4.3243 0.827

9 -0.155 -0.137 6.3436 0.705 10 0.143 0.191 8.0895 0.620

Manufaktur

AC PAC Q-Stat Prob

1 0.164 0.164 1.9817 0.159 2 -0.001 -0.028 1.9817 0.371 3 0.159 0.168 3.9060 0.272 4 -0.062 -0.124 4.2017 0.379 5 0.085 0.137 4.7646 0.445 6 0.061 -0.018 5.0629 0.536 7 -0.026 0.009 5.1171 0.646 8 0.016 -0.025 5.1391 0.743 9 -0.088 -0.083 5.7799 0.762 10 -0.060 -0.028 6.0894 0.808

Perdagangan dan jasa

AC PAC Q-Stat Prob

1 0.247 0.247 4.5147 0.034 2 0.003 -0.061 4.5156 0.105 3 0.173 0.200 6.7909 0.079 4 -0.079 -0.195 7.2707 0.122 5 -0.094 0.001 7.9706 0.158 6 -0.098 -0.141 8.7287 0.189 7 0.026 0.161 8.7855 0.268 8 0.014 -0.065 8.8013 0.359 9 -0.047 0.020 8.9885 0.438 10 -0.119 -0.219 10.191 0.424

Pertambangan

AC PAC Q-Stat Prob

1 0.234 0.234 4.0680 0.044 2 0.040 -0.016 4.1897 0.123 3 0.195 0.200 7.0982 0.069 4 -0.063 -0.170 7.4020 0.116 5 -0.121 -0.068 8.5603 0.128 6 -0.056 -0.059 8.8072 0.185

7 0.062 0.149 9.1228 0.244 8 0.009 -0.014 9.1300 0.331 9 -0.152 -0.170 11.058 0.272 10 -0.171 -0.191 13.535 0.195

Pertanian

AC PAC Q-Stat Prob

1 0.120 0.120 1.0728 0.300 2 -0.077 -0.093 1.5155 0.469 3 0.076 0.100 1.9590 0.581 4 -0.139 -0.176 3.4544 0.485 5 -0.023 0.041 3.4974 0.624 6 -0.086 -0.138 4.0846 0.665 7 0.040 0.118 4.2123 0.755 8 0.039 -0.045 4.3374 0.825 9 -0.128 -0.087 5.6974 0.770 10 -0.210 -0.250 9.4347 0.491

Properti

AC PAC Q-Stat Prob

1 0.073 0.073 0.3927 0.531 2 0.073 0.068 0.7951 0.672 3 0.087 0.078 1.3703 0.712 4 -0.215 -0.235 4.9542 0.292 5 -0.072 -0.055 5.3579 0.374 6 -0.043 -0.009 5.5079 0.481 7 0.009 0.068 5.5139 0.598 8 -0.013 -0.056 5.5281 0.700 9 0.066 0.044 5.8885 0.751 10 -0.126 -0.166 7.2349 0.703

DAFTAR PUSTAKA

Agung Nugroho, Puguh. 2010. “Pengujian Taraf Akurasi Model-model Volatilitas Dalam Menduga Nilai Risiko Obligasi (Studi Kasus : Obligasi Indon 14)”. Semarang: Universitas Diponegoro

Darmadji, Tjiptono dan Hendy M Fakhruddin, 2006. Pasar Modal Di Indonesia pendekatan tanya jawab, Jakarta: Salemba Empat

Djalal, Nachrorowi dan Hardius Usman, 2006. Penekatan Populer dan Praktis Ekonometrika Untuk Analisis Ekonomi dan Keuangan, Jakarta : Lembaga Penerbit Fakultas Ekonomi Universitas Indonesia.

Detik finance Online, 2013. Transaksi Saham Naik Jadi Rp 6,3 Triliun per Hari, Didominasi Investor Lokal.

Maret 2013).

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Guidi, F. 2008. “European Central Bank and Federal Reserve USA : Monetary Policy Effects on the Returns Volatility of the Italian Stock Market Index Mitbel”.Munich Personal Repec Archieve Papers, no.10759.

Hermuningsih, Sri, 2012. Pengantar Pasar Modal Indonesia, Yogyakarta: UPP STIM YKPN

Jogiyanto, H M, 2000. Teori Portofolio dan Analisis Investasi, edisi III, Yogyakarta: BPFE-YOGYAKARTA.

Kompas.com, 2008. Tahun 2008, IHSG Berprestasi dan Terpuruk.

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Putra, Mario D. 2009. “Analisis Pengaruh Kebijakan Moneter Terhadap Volatilitas Return di Pasar Saham Bursa Efek Indonesia”. Bogor: Institut Pertanian Bogor

Simatupang, Mangasa, 2010. Pengetahuan Praktis Investasi Saham dan Rersa Dana, Jakarta: Mitra Wacana Media.

Situmorang, M Paulus, 2008. Pengantar Pasar Modal, Jakarta: Mitra Wacana Media.

Tandelilin, Eduardus, 2001. Analisis Investasi dan Manajemen Portofolio,

Yogyakarta: BPFE-YOGYAKARTA.

Thabelo, Nemorani. 2012. “ Impact of monetary policy on stock prices: Evidence from Botswana”. Unpublished BA Economics Project, University of Botswana.

Warjio, Perry, 2004. Mekanisme Transmisi Kebijakan Moneter Di Indonesia, Jakarta: Pusat Pendidikan dan Studi Kebanksentralan (PPSK) BI.

Widoatmodjo, Sawidji, 2009. Pasar Modal Indonesia Pengantar dan Studi Kasus, Jakarta:Ghalia Indonesia.

BAB III

METODE PENELITIAN

3.1Jenis Penelitian

Jenis Penelitian yang dilakukan adalah penelitian asosiatif kausal. Yaitu penelitian yang bertujuan untuk menganalisis hubungan antara satu variabel dengan variabel lainnya dan bagaimana suatu variabel mempengaruhi variabel lainnya.Dalam penelitian ini akan meganalisis pengaruh kebijakan moneter melalui instrumen penetapan suku bunga acuan terhadap return saham berupa

capital gain/loss dan indeks harga saham sektoral di Bursa Efek Indonesia. 3.2 Batasan Operasional

Dalam Penelitian ini akan menyajikan analisis secara deskriptif dan kuantitatif dengan menggunakan ekonometrika mengenai pengaruh kebijakan moneter terhadap return saham di pasar saham Bursa Efek Indonesia (BEI). Selain itu, analisis dalam skripsi ini hanya terbatas pada analisis hubungan antara suku bunga Bank indonesia yang dijadikan sebagai acuan bunga pada pasar keuangan dengan indeks harga saham gabungan sebagai cara untuk melihat return saham berupa

capital gain/loss dan indeks tiap sektor dipasar saham selama periode 2007-2012. Adapun faktor-faktor eksternal yang mungkin mempengaruhi dalam analisis dianggap konstan dalam skripsi ini.

3.3Definisi Operasional

Secara garis besar definisi operasional dari variabel-variabel yang digunakan didalam penelirian ini adalah sebagai berikut:

Dalam penelitian ini yang menjadi variabel dependen adalah BI rate atau suku bunga Bank Indonesia. Merupakan tingkat suku bunga yang ditetapkan oleh BI sebagai patokan bagi suku bunga pinjaman maupun simpanan bagi bank dan atau lembaga-lembaga keuangan di seluruh Indonesia.

2. Variabel Independen

Dalam penelitian ini yang menjadi variabel independen yaitu return saham. Yaitu tingkat keuntungan dari indeks pasar yang akan diterima oleh para investor berupa capital gain (loss) yang merupakan selisih laba (rugi) yang dialami oleh pemegang saham karena harga saham sekarang relatif lebih tinggi (rendah) dibandingkan harga saham sebelumnya. Data return tersebut akan dapat diketahui melalui pergerakan indeks harga saham gabungan dan indeks harga saham sektoral. Indeks harga saham gabungan (IHSG) Merupakan angka indeks harga saham yang telah disusun dan dihitung sedemikian rupa sehingga menghasilkan trend. Sedangkan angka indeks itu sendiri adalah angka yang dibuat sedemikian rupa sehingga dapat dipergunakan untuk membandingkan kegiatan atau peristiwa berupa perubahan harga saham dari waktu ke waktu. Sedangkan Indeks harga saham sektoral adalah indeks yang menggunakan semua perusahaan yang tercatat yang termasuk dalam masing-masing sektor. Indeks sektoral terdiri dari sepuluh sektor yang ada di BEI yaitu sektor Pertanian, Pertambangan, Industri Dasar, Aneka Industri, Barang Konsumsi, Properti, Infrastruktur, Keuangan, Perdangangan dan Jasa, dan Manufatur

3.4 Jenis dan Sumber Data

Jenis data yang digunakan dalam penelitian ini adalah data sekunder. Data sekunder merupakan data yang diperoleh langsung dari instansi-instansi resmi atau publikasi- publikasi resmi. Dalam penelitian ini data yang digunakan adalah data time series bulanan dari tahun 2007 sampai dengan tahun 2012. Data yang digunakan pada penelitian ini adalah indeks harga saham gabungan dan indeks harga saham sektoral yang ada di pasar saham dan suku bunga acuan yang ditetapkan oleh Bank Indonesia. Data tersebut diperoleh dari Bank Indonesia (BI), Bursa Efek Indonesia (BEI), dan instansi-instansi terkait lainnya.

3.5 Metode Pengumpulan Data

Metode pengumpulan data yang digunakan dalam penelitian ini adalah metode studi kepustakaan (library ressearch), yaitu penelitian yang dilakukan melalui bahan-bahan kepustakaan berupa buku-buku, tulisan-tulisan ilmiah, artikel, jurnal, serta laporan-laporan penelitian yang berhubungan dengan penelitian ini. Sedangkan teknik pengumpulan data yang digunakan dalam penelitian ini adalah dengan melakukan pencatatan langsung berupa data urut waktu ( time series) selama kurun waktu januari 2007 s/d Desember 2012 yang Bank Indonesia (www.bi.go.id), Bursa Efek Indonesia (BEI) dan yahoo finance

3.6Pengolahan Data dan Metode Analisis

Dalam penulisan skripsi, penulis menggunakan program komputer Microsoft Excel dan Eviews 6 untuk menganalisis data serta pembuatan tabei, grafik, dan

mempermudah perhitungan. Dalam penelitian ini, metode analisis data yang digunakan penulis adalah model ARCH-GARCH.

3.6.1 Model ARCH-GARCH

Model Auto Regressive Conditional Heteroscedasticity (ARCH) dan

Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) adalah suatu model yang tidak memandang heterokedastisitas sebagai permasalahan, tetapi justru memanfaatkan heterokedasitisas dalam error dengan tepat yang akan diperoleh estimator yang lebih efisien. GARCH cukup baik untuk memodelkan data yang memiliki varian yang berubah-ubah seiring dengan perubahan waktu. Aplikasi yang mempunyai karakteristik seperti ini biasanya pada pemodelan

return dari pasar modal, inflasi, atau interest rate.

Model yang dikenalkan oleh Robert Engel (1982) biasanya mengindikasikan sebagai Autoregressive Conditional Heteroscedasticity Model (ARCH). Pengembangan model diajukan oleh Bollerslev (1986) yang menemukan

Generalized ARCH (GARCH) models. Model ini mempunyai kecenderungan

yang sama sebagai model ARCH, walaupun memperbolehkan varians bersyarat untuk bervariasi tidak hanya dalam fungsi dari eror sebelumnya, tetapi juga oleh

lags-nya.

Misalnya kita memiliki model persamaan regresi:

�� = �0+ �1�1� + �2�2� + �� (3.1)

��2atau varian �� heteroskedastisitas, dan mengikuti persamaan berikut:

��2 = �0+ �1��−2 1 ; ��2 = ��� (��) (3.2) Perhatikan bahwa ��� (�_�) dijelaskan oleh dua komponen:

- Komponen konstanta: �₀

- Komponen variabel: �₁�_�−2 ₁ ; yang disebut komponen ARCH

Model ARCH diatas, dimana ��� (��) tergantung hanya pad volalitas satu periode lalu, seperti pada �_�2 = �0+ �1��−2 1, disebut model ARCH (I). Sedangkan secara umum, bila ��� (��) tergantung pada volatilitas beberapa periode lalu seperti �_�2 = �0+ �1��−2 1+ �2��−2 2+ … . .�� ��−�2 disebut model ARCH (p). Atau ditulis dengan:

��2 = �0+ ∑�_�=1�1 ��−1 (3.3) Pada model ini, agar varian menjadi positif (Var(�2)>0), maka harus dibuat pembatasan, yaitu: �₀ > 0 dan 0 < �₁ <1.

Pada model ARCH (p) di atas, dengan jumlah p yang relatif besar akan mengakibatkan banyaknya parameter yang harus diestimasi. Semakin banyak parameter yang harus diestimasi dapat mengakibatkan presisisi dari estimator tersebut berkurang. Untuk mengatasi masalah tersebut, agar parameter yang diestimasi tidak terlalu banyak, ���(�_�) dapat dijadikan model berikut:

��2 = �0+ �1�_�−2 1+ �1�_�−2 1 (3.4) Model ini disebut model GARCH, karena �_�2 tergantung pada �_�−2 ₁dan

��−2 1yang masing-masing mempunyai lag waktu satu. Sama halnya dengan model ARCH, agar varian menjadi positif (Var(�2)>0), maka pada model ini juga harus dibuat pembatasan, yaitu: �₀ > 0; �₁ dan �₁≥ 0; dan �₁ + �₁ < 1.

Sebagaimana model ARCH, maka model GARCH ini juga dapat diestimasi dengan teknik Maximum Likelihood.

��2 = �0+ �1��−2 1+ … . + �_{���−�}2 + �₁�_�−2 ₁+ … . + �_��_�−�2 atau ditulis dengan: ��2 = �0+ ∑�_�=1�� ��−2 1 + ∑ ��

�

�=1 ��−�

� _(3.5)

Model diatas disebut model GARCH (p,q)

Dari model diatas terlihat bahwa besaran ��� (�_�) selain diduga tergantung pada �2 juga tergantung pada �2 pada masa lalu

Dalam permodelan penelitian ini akan digunakan model GARCH. Untuk mengevaluasi hubungan antara return saham dengan kebijakan moneter berupa instrumen suku bunga. Sehingga akan terbentuk persamaan sebagai berikut:

Return = �₀ + SBI �_�2 + �_�

model yang memunculkan �_�2 pada regresornya disebut model ARCH –M (ARCH

-in -mean).

Var (��) = �_�2 dapat dinyatakan dalam bentuk GARCH (p,q):

Return = �0+ ������−2 1+ �1��−2 1

Dalam persamaan GARCH tersebut variabel SBI merupakan suku bunga acuan dari bank sentral yaitu Bank Indonesia.

Kriteria model yang terbaik adalah memiliki ukuran kebaikan model yang baik dan koefisien yang nyata. Mengacu kepada Modul Praktikum Pelatihan Time Series Analysis, kerjasama antara Bank Indonesia dan Lembaga Penelitian dan Pemberdayaan Masyarakat (LPPM) dan Departemen Statistika Institut Pertanian Bogor (IPB) tahun 2006 maka langkah analisis ARCH/GARCH adalah sebagai berikut :

1. Penyiapan Data Langkah pertama di dalam analisis ini adalah penyiapan data yang akan dianalisis. Piranti lunak yang akan digunakan untuk

melakukan pemodelan ARCH menggunakan software EViews 6.0. Selanjutnya menentukan periode waktu data yang akan digunakan, yaitu apakah tahunan, semester, triwulanan, bulanan, mingguan, atau harian. 2. Pemeriksaan Pola Data, Pemeriksaan ini berguna untuk penentuan strategi

mean model yang disusun dan evaluasi awal keragaman data. Dari plot tersebut akan terlihat kecenderungan pola data dan simpangan data apakah cenderung konstan atau tidak konstan.

3. Analisis Mean, Setelah strategi bagi model untuk mean model sudah diperoleh dari tahapan pemeriksaan plot, langka berikutnya adalah analisis mean model tersebut.

4. Evaluasi Residual Dari Mean Model Setelah analisis mean model dilakukan, langkah berikutnya adalah memeriksa apakah terdapat ketidakhomogenan variance dari residual mean model. Selanjutnya, pemeriksaan apakah terdapat ARCH pada residual dapat dilakukan melalui Uji LM (Langrang Multiplier) dari lag 1 berurut kepada lag berikutnya. Bila terdapat ARCH hingga lag 12 maka dilakukan pemodelan dengan menggunakan model GARCH.

5. Analisis ARCH/GARCH Terhadap Data Setelah menentukan model yang akan digunakan, langkah berikutnya adalah menentukan ordo model. 6. Diagnostik Model Hasil analisis di atas masih memerlukan pemeriksaan

terhadap kenormalan data mengingat metode pendugaan yang digunakan adalah maximum likelihood serta evalueasi apakah masih terdapat heterokedastisitas pada residual. Untuk mengatasi ketidaknormalan

residual, proses pendugaan variance dilakukan dengan menggunakan metode Bollerslev-Wooldridge. Penggunaan metode Bollerslev-Wooldrifge ini lebih kepada memperbaiki pendugaan variance pada komponen ”variance model” akibat tidak normalnya residual. Hal ini dapat dilihat dari standard error komponen varian model yang menggunakan metode ini.

BAB IV

HASIL DAN PEMBAHASAN

4.1Kebijakan Moneter dan Volatilitas Return

Pasar modal yang maju dan berkembang pesat merupakan tujuan utama dari banyak negara. Hal ini dikarenakan pasar modal memiliki peran besar bagi perekonomian suatu negara. Dalam perekonomian, pasar modal memiliki dua fungsi sekaligus yaitu fungsi ekonomi dan fungsi keuangan. Pasar modal dikatakan menjalankan fungsi ekonomi karena pasar modal menyediakan fasilitas yang mempertemukan dua kepentingan yaitu pihak yang memiliki kelebihan dana dan pihak yang memerlukan dana. Dengan adanya pasar modal, maka pihak yang memiliki kelebihan dana dapat menginvestasikan dana tersebut dengan harapan memperoleh imbal hasil (return), sedangkan pihak emiten dapat memanfaatkan dana tersebut untuk kepentingan investasi. Sedangkan pasar modal dikatakanmemiliki fungsi keuangan karena memberikan kemungkinan dan kesempatan memperoleh imbal hasil bagi pemilik dana, sesuai dengan karakteristik investasi yang dipilih. Dengan adanya pasar modal, diharapkan aktivitas perekonomian dapat meningkat karena pasar modal merupakan alternatif pendanaan bagi perusahaan, sehingga dapat beroperasi dengan skala yang lebih besar, dan selanjutnya akan meningkatkan pendapatan perusahaan dan kemakmuran masyarakat luas (Darmadji, 2006:2).

Hal inilah yang membuat pentingnya bagi suatu negara untuk merealisasikan pasar modal yang maju dan modern. Salah satu yang dapat dilakukan pemerintah yaitu dengan terus menjaga kestabilan ekonominya guna menciptakan

pertumbuhan ekonomi. Karena pertumbuhan ekonomi yang baik secara umum menunjukkan tingkat perbaikan kesejahteraan masyarakat, dan hal ini biasanya diikuti dengan kegiatan pasar modal yang semakin bergairah. Sebaliknya, kondisi ekonomi yang lesu akan ditunjukkan juga dari kegiatan pasar modal yang melemah. Namun perkembangan pasar saham yang semakin modern dan semakin mengglobal menyebabkan keadaan dipasar saham semakin sulit untuk diprediksi. Hal ini dikarenakan semakin banyak faktor yang mempengaruhi pasar modal. Nilai transaksi yang sedemikian besar perharinya dan berbagai faktor pembentuk yang dapat mempengaruhi merupakan hal yang menyebabkan return di pasar saham semakin mengalami volatilitas. Pada saat tertentu dapat terjadi keadaan dimana return dari pasar tersebut menjadi sangat tinggi, disisi lain nilai tersebut dapat turun dengan sangat tajam hanya dalam tempo beberapa saat. Pergerakan yang demikian disebabkan oleh berbagai faktor penentu, baik dari dalam ataupun dari luar yang menyebabkan para pelaku di pasar saham melakukan keputusan pembelian atau penjualan di pasar saham yang kemudian mempengaruhi pergerakan tersebut. Namun dalam menjaga kestabilan ekonomi, pemerintah haruslah bijak dalam menjalankan kebijakannya. Karena apabila bank sentral menggunakan kebijakan moneter dengan meningkatkan suku bunga guna mengendalikan inflasi, pada gilirannya akan mempengaruhi pasar saham, dimana hal ini akan menghambat pertumbuhan indeks harga saham di pasar modal.

Untuk mengetahui lebih lanjut akan lebih menarik jika menganalisis pengaruh kebijakan moneter terhadap pergerakan tingkat pengembalian (return) di pasar saham, sehingga dapat menjawab pertanyaan apakah kebijakan moneter

melalui instrumen penetapan suku bunga acuan dapat mempengaruhi pasar saham, lebih lanjut akan dianalisis dengan menggunakan model ekonometrika untuk melihat kemungkinan keterkaitan antara suku bunga dengan return saham.

4.2 Deskripsi Data

Penelitian ini menganalisis pengaruh suku bunga acuan (BI rate) sebagai instrumen kebijakan moneter terhadap volatilitas return saham berupa capital gain/loss yang dilihat dari pergerakan harga saham di Bursa Efek Indonesia, dan juga melihat pengaruh dari suku bunga acuan terhadap volatilitas dari tiap sektor di pasar saham melalui indeks harga saham sektoral yaitu sektor Pertanian, Pertambangan, Industri Dasar, Aneka Industri, Barang Konsumsi, Properti, Infrastruktur, Keuangan, Perdangangan dan Jasa, serta Manufatur.

Gambar 4.1 Return Indeks Harga Saham Gabunga Periode Januari 2007 s.d Desember 2012

-.4 -.3 -.2 -.1 .0 .1 .2 .3

2007 2008 2009 2010 2011 2012

-.6 -.4 -.2 .0 .2 .4

2007 2008 2009 2010 2011 2012

PERTANIAN -.6 -.4 -.2 .0 .2 .4 .6

2007 2008 2009 2010 2011 2012

PERTAMBANGAN -.4 -.3 -.2 -.1 .0 .1 .2 .3

2007 2008 2009 2010 2011 2012 INDUSTRIDASAR 4 3 2 1 0 1 2 3

2007 2008 2009 2010 2011 2012

ANEKAINDUSTRI 16 12 08 04 00 04 08 12 16 20

2007 2008 2009 2010 2011 2012

KONSUMSI .3 .2 .1 .0 .1 .2

2007 2008 2009 2010 2011 2012 PROPERTI

Gambar 4.2 Return Indeks Harga Saham Sektoral Periode Januari 2007 s.d Desember 2012

Hal yang penting untuk diamati dalam penelitian ini adalah menjawab pertanyaan apakah kebijakan moneter melalui instrumen suku bunga di Indonesia mempunyai pengaruh terhadap volatilitas return di pasar saham. Pada gambar 4.1 akan teramati fluktuasi dari return di pasar saham berupa return berdasarkan

-.3 -.2 -.1 .0 .1 .2

2007 2008 2009 2010 2011 2012 INFRASTRUKTUR

-.3 -.2 -.1 .0 .1 .2 .3

2007 2008 2009 2010 2011 2012 KEUANGAN

-.4 -.3 -.2 -.1 .0 .1 .2

2007 2008 2009 2010 2011 2012 PERDAGANGANJASA

-.3 -.2 -.1 .0 .1 .2

2007 2008 2009 2010 2011 2012 MANUFAKTUR

ABSTRACT

This study aimed to analyze the effect of the nonetery policy through interest rate of Bank Indonesia on stock returns in the Indonesia Stock Exchange, which will be seen in this study is to prove whether or not the influence of interest rates on return volatility in the stock price index and stock index sector in the stock market. The method used in the analysis is the GARCH model, which is a test that see whether there is an effect.

The analysis showed that the rate of Bank Indonesia doesn’t affect the volatility of the stock price index return. At the sectoral stock indices, interest rate of Bank Indonesia return volatility affects the infrastructure sector, trade and services, mining, agriculture and property. While violatilitas return on the various sectors of industry, basic industry, the financial sector, consumer sector and the manufacturing sector is not affected by the interest rate of Bank Indonesia.

keywords: interest rate of Bank Indonesia, stock return, GARCH, stock price index, stock index sectoral

DAFTAR ISI

KATA PENGANTAR ... i

ABSTRAK ... iv

ABSTRACT ... v

DAFTAR ISI ... vi

DAFTAR GAMBAR ... viii

DAFTAR TABEL ... ix

DAFTAR LAMPIRAN ... x

BAB I PENDAHULUAN 1.1 Latar Belakang ... 1

1.2 Rumusan Masalah ... 5

1.3 Tujuan Penelitian ... 5

1.4 Manfaaat Penelitian ... 6

BAB II TINJAUAN PUSTAKA 2.1 Tinjauan Teoritis... 7

2.1.1 Kebijakan Moneter ... 7

2.1.2 BI Rate ... ...11

2.1.3 Mekanisme Transmisi Kebijakan Moneter ... 12

2.1.4 Pasar Modal ... 15

2.1.5 Indeks Harga Saham ... 18

2.1.6 Return Saham ... 23

2.2 Penelitian Terdahulu ... 25

2.3 Kerangka Konseptual ... 26

2.4 Hipotesis Penelitian ... 27

BAB III METODE PENELITIAN 3.1 Jenis Penelitian ... 28

3.2 Batasan Operasional ... 28

3.3 Defenisi Operasional ... 28

3.4 Jenis dan Sumber Data ... 30

3.5 Metode Pengumpulan Data ... 30

3.6 Pengolahan Data dan Metode Analisis ... 30

3.6.1 Model ARCH-GARCH ... 31

BAB IV HASIL DAN PEMBAHASAN 4.1 Kebijakan Moneter dan Volatilitas Return ... 36

4.2 Deskripsi Data ... 38

4.3 Hasil Empiris ... 41

4.3.1 Model GARCH Indeks Harga Saham Gabungan ... 41

4.3.2 Model GARCH Indeks Saham Persektor...45

BAB V KESIMPULAN DAN SARAN

5.1 Kesimpulan ... 52 5.2 Saran ... 53

DAFTAR PUSTAKA ... 54

DAFTAR TABEL

Nomor Judul Halaman

Tabel 4.1 Nilai AIC dan SBC Model GARCH Pada IHSG ... 42

Tabel 4.2 GARCH (1.1) Model Estimasi Dari IHSG ... 43

Tabel 4.3 Nilai AIC dan SBC Model GARCH Saham Sektoral ... 45

Tabel 4.4 Hasil ARCH LM Test pada tiap Model GARCH ... 47

Tabel 4.5 Statistika Deskriptif Data Return Saham Sektoral ... 47

Tabel 4.6 Statistika Deskriptif Model ARCH-M ... 49

Tabel 4.7 Koefisien GARCH Model Estimasi Saham Sektoral ... 50

DAFTAR GAMBAR

No.Gambar Judul Halaman

1.1 Pergerakan IHSG Periode 2007-2012 ... 2

2.1 Mekanisme Transmisi Moneter Sebagai Black Box ... 14

2.2 Kerangka Pemikiran ... 27

4.1 Return Indeks IHSG Periode 2007-2012 ... 38

DAFTAR LAMPIRAN

No.Lampiran Judul Halaman

1. Estimasi Model GARCH ... 56

2. Statistik Deskriptif ... 83

3. ARCH LM Test ... 85

4. Uji Kebebasan Galat ... 97