Name of the company: Boon Koon Group Berhad (7187.KL) Industrial Products

In the following report we are going to conduct and ARCH Model for Boon Koon Group

Berhad stock price. We will be looking whether today’s stock price of Boon Koon Group

Berhads a function of yesterday’s price.

Before we started the ARCH model of Boon Koon Group Berhad, we should recall that is that

Boon Koon Group Berhad has a unit root test? The answer is YES. So now we going to run

ARCH model with Boon Koon Group Berhad.

However, before we could run for an ARCH MODEL there two other fundamentals

conditions that we should fulfill.

1. There should be a clustering volatility

2. ARCH test should be rejected, meaning there should ARCH effect.

Testing weather there hase a clustering volatility:

The Graph of RCLOSE:

Graph interpretation: as we see from the graph highly volatility followed by high period.

There has a clustering volatility showed by the graph.

Estimate equation: for testing weather or not to continue with the ARCH model. Dependent Variable: RCLOSEMethod: Least SquaresDate: 03/05/16 Time: 10:40Sample (adjusted): 3 2755Included observations: 2753 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 114.3088 1.887921 60.54743 0.0000RCLOSE(-1) -0.143448 0.018874 -7.600447 0.0000

R-squared 0.020567 Mean dependent var 99.96832Adjusted R-squared 0.020211 S.D. dependent var 3.462959S.E. of regression 3.427786 Akaike info criterion 5.302432Sum squared resid 32323.47 Schwarz criterion 5.306734Log likelihood -7296.798 Hannan-Quinn criter. 5.303986F-statistic 57.76680 Durbin-Watson stat 2.014995Prob(F-statistic) 0.000000

Prob(F-statistic) is 0.000000, we are going to reject the NULL. Therefore we can conclude

that there is an ARCH effect. Finally, since there is a clustering volatility and there is an

ARCH effect, thus we have all the validity to run the ARCH model.

Estimate equation of ARCH:

Dependent Variable: RCLOSEMethod: ML ARCH - Normal distribution (BFGS / Marquardt steps)Date: 03/05/16 Time: 10:41Sample (adjusted): 3 2755Included observations: 2753 after adjustmentsConvergence achieved after 34 iterationsCoefficient covariance computed using outer product of gradientsPresample 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 115.9539 2.144427 54.07220 0.0000RCLOSE(-1) -0.160014 0.021439 -7.463529 0.0000

Variance Equation

C 0.850238 0.063403 13.41000 0.0000RESID(-1)^2 0.110323 0.009229 11.95353 0.0000GARCH(-1) 0.822509 0.012820 64.15688 0.0000

R-squared 0.020282 Mean dependent var 99.96832Adjusted R-squared 0.019926 S.D. dependent var 3.462959S.E. of regression 3.428283 Akaike info criterion 5.177700Sum squared resid 32332.85 Schwarz criterion 5.188452Log likelihood -7122.104 Hannan-Quinn criter. 5.181584Durbin-Watson stat 1.983747

From the above statistic we got that coefficient RESID(-1)^2 is 0.110323 and GRACH(-1) is

0.822509. Then we know coefficient 0.110323+0.822509=0.932832. The coefficient is near

to 1, highly volalitity highly persistance.

How long it persistance:

Formula: -0.5/ln(RESID(-1)^2+ GRACH(-1))

-0.5/ln(0.932832)=7.191124

It takes around 7 days before the volalitity change.

Residual Diagnostics ARCH LM test:

Heteroskedasticity Test: ARCH

F-statistic 0.000203 Prob. F(1,2750) 0.9886Obs*R-squared 0.000203 Prob. Chi-Square(1) 0.9886

Test Equation:Dependent Variable: WGT_RESID^2Method: Least SquaresDate: 03/05/16 Time: 10:45Sample (adjusted): 4 2755Included observations: 2752 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.999366 0.079471 12.57526 0.0000WGT_RESID^2(-1) 0.000272 0.019070 0.014246 0.9886

R-squared 0.000000 Mean dependent var 0.999637Adjusted R-squared -0.000364 S.D. dependent var 4.046645S.E. of regression 4.047380 Akaike info criterion 5.634743Sum squared resid 45048.54 Schwarz criterion 5.639046Log likelihood -7751.407 Hannan-Quinn criter. 5.636298F-statistic 0.000203 Durbin-Watson stat 1.999922Prob(F-statistic) 0.988635

The probility of F-statistic 0.9886>0.05 , it is not significant. So we can conclude that the

model is good. Today the Boon Koon Group Berhad stock price is a function of yesterday’s

Boon Koon Group Berhad stock price.

Grach Gragh:

Graph interpretation: from the graph interpretation we see that, there are the clustering

volatility. Therefore, highly volatility high risk.

GARCH: Dependent Variable: RCLOSEMethod: ML ARCH - Normal distribution (BFGS / Marquardt steps)Date: 03/05/16 Time: 10:47Sample (adjusted): 3 2755Included observations: 2753 after adjustmentsConvergence achieved after 37 iterationsCoefficient covariance computed using outer product of gradientsPresample variance: backcast (parameter = 0.7)GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-1)^2*(RESID(-1)<0) + C(6)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob.

C 115.9536 2.158717 53.71414 0.0000RCLOSE(-1) -0.160012 0.021564 -7.420381 0.0000

Variance Equation

C 0.850121 0.063383 13.41238 0.0000RESID(-1)^2 0.110236 0.009974 11.05258 0.0000

RESID(-1)^2*(RESID(-1)<0) 0.000224 0.013770 0.016253 0.9870GARCH(-1) 0.822507 0.012911 63.70713 0.0000

R-squared 0.020282 Mean dependent var 99.96832Adjusted R-squared 0.019926 S.D. dependent var 3.462959S.E. of regression 3.428284 Akaike info criterion 5.178426Sum squared resid 32332.86 Schwarz criterion 5.191329Log likelihood -7122.104 Hannan-Quinn criter. 5.183088Durbin-Watson stat 1.983749

The GRACH(-1) coefficient is 0.922507, probility is 0.0000.

There is no difference in effect volatility in good news and bad news. Its may effected same

for the stock price of the company.