Research Methodology Descriptive Statistics

ϱϲ Figure 4.6.3 Partial t-test of FHSI 3 to JII Figure 4.6.4 Partial t-test of FHSI 4 to JII 3. VECM Exchange Rate LNEXRATE Partial t-test Result VECM estimation result defines that Exchange Rate LNEXRATE t-statistic is 0.873 in lag 1, 0.709 in lag 2, -0.886 in lag 3 and -1.497 in lag 4 with degree of freedom df 60-6-1 = 53 LQFRQILGHQFHOHYHOĮ ,QDGGLWLRQW-table of t-test is 1.296. Therefore, t-statistic of LNEXRATE in lag 4 -1.497 t- table 1.296 means that we reject Ho and accept Ha or the model is significant. It can be illustrated by the illustration below: ͲϮ͘ϲϲϬ Ϯ͘ϲϲϬ ZĞũĞĐƚĞĚ ZĞũĞĐƚĞĚ ĐĐĞƉƚĞ Ͳϯ͘ϯϳϳ Ͳϭ͘ϲϳϭ ϭ͘ϲϳϭ ZĞũĞĐƚĞĚ ZĞũĞĐƚĞĚ ĐĐĞƉƚĞĚ Ͳϭ͘ϵϱϱ ϱϳ Figure 4.6.5 Partial t-test of LNEXRATE 4 to JII 4. VECM Consumer Price Index CPI Partial t-test Result VECM estimation result defines that Consumer Price Index CPI t-statistic is -0.810 in lag 1, -0.452 in lag 2, -0.571 in lag 3 and - 1.565 in lag 4 with degree of freedom df 60-6-1 = 53 in 80 FRQILGHQFHOHYHOĮ ,QDGGLWLRQW-table of t-test is 1.296. Therefore, t-statistic of CPI in lag 4 -1.565 t-table 1.296 means that we reject Ho and accept Ha or the model is significant. It can be illustrated by the illustration below: Figure 4.6.6 Partial t-test of CPI 4 to JII 5. VECM Money Supply LNM2 Partial t-test Result VECM estimation result defines that Money Supply LNM2 t- statistic is 0.145 in lag 1, 0.461 in lag 2, 0.195 in lag 3 and 1.636 in lag 4 with degree of freedom df 60-6-1 = 53 in 80 Ͳϭ͘Ϯϵϲ ϭ͘Ϯϵϲ ZĞũĞĐƚĞĚ ZĞũĞĐƚĞĚ ĐĐĞƉƚĞĚ ϭ͘ϰϵϳ Ͳϭ͘Ϯϵϲ ϭ͘Ϯϵϲ ZĞũĞĐƚĞĚ ZĞũĞĐƚĞĚ ĐĐĞƉƚĞĚ Ͳϭ͘ϱϲϱ ϱϴ FRQILGHQFHOHYHOĮ ,QDGGLWLRQW-table of t-test is 1.296. Therefore, t-statistic of LNM2 in lag 4 1.636 t-table 1.296 means that we reject Ho and accept Ha or the model is significant. It can be illustrated by the illustration below: Figure 4.6.7 Partial t-test of LNM2 4 to JII 6. VECM Bank Indonesia Interest Rate BIR Partial t-test Result VECM estimation result defines that Bank Indonesia Interest Rate BIR t-statistic is 1.177 in lag 1, -0.454 in lag 2, -0.130 in lag 3 and -1.598 in lag 4 with degree of freedom df 60-6-1 = 53 in 80 confidence level Į ,Q DGGLWLRQ W-table of t-test is 1.296. Therefore, t-statistic of BIR in lag 4 -1.598 t-table 1.296 means that we reject Ho and accept Ha or the model is significant. It can be illustrated by the illustration below: Figure 4.6.8 Partial t-test of BIR 4 to JII Ͳϭ͘Ϯϵϲ ϭ͘Ϯϵϲ ZĞũĞĐƚĞĚ ZĞũĞĐƚĞĚ ĐĐĞƉƚĞĚ Ͳϭ͘ϱϵϴ Ͳϭ͘Ϯϵϲ ϭ͘Ϯϵϲ ZĞũĞĐƚĞĚ ZĞũĞĐƚĞĚ ĐĐĞƉƚĞĚ ϭ͘ϲϯϲ ϱϵ

4.6.2 VECM Simultaneous f-test Result

To observe the significant influence of the independent variables to the dependent variable simultaneously, we conduct the simultaneous f-test examination. The VECM estimation result describes that VECM f-statistic is 1.918 with degree of freedom df for N1 is 7-1 = 6 and df for N2 is 60-7 LQFRQILGHQFHOHYHOĮ ,QDGGLWLRQWKHI-table of f- test is 1.87. Therefore, VECM f-statistic is 1.918 f-table 1.87 means that we reject Ho and accept Ha or the model is simultaneously significant. It can be illustrated in the illustration below: Figure 4.6.9 VECM Simultaneous f-test of DJIM, FHSI, LNEXRATE, CPI, LNM2 and BIR

4.7 Variance Decomposition Test

The variance decomposition showed the further evidence of relationships among the variables in this research. It showed the proportion of the forecast error of one variable due to the other variables. The results from variance decomposition are reported in table 4.8. Ͳϭ͘ϴϳ ϭ͘ϴϳ ZĞũĞĐƚĞĚ ZĞũĞĐƚĞĚ ĐĐĞƉƚĞĚ ϭ͘ϵϭϴ ϲϬ Table 4.7 VDC Result Variance Decomposition of DJII: Period S.E. DJII DDJI M DFHSI DLNEXR ATE DCPI DLNM2 DBIR 1 19.171 100 0.000 0.0000 0.0000 0.0000 0.0000 0.000 6 33.293 59.889 9.692 16.080 1.501 6.987 1.333 4.514 12 46.271 36.264 7.625 36.650 1.224 9.847 1.138 7.250 18 49.718 33.429 7.190 38.982 1.375 10.111 1.154 7.755 24 51.002 32.529 7.577 38.981 1.706 9.750 1.161 8.293 Table 4.7 defines variable decomposition of DJII. It explains how DDJIM, DFHSI, DLNEXRATE, DCPI, DLNM2 and DBIR influence DJII. In the 1st month DJII is 100 influenced by itself. Furthermore, the influence of variable DJII to DJII itself is decreased to 32.52 in the 24th month. Table 4.7 also defines that variable DJII is 0.00 influenced by DDJIM in the 1st month. And so, the influence of DDJIM to DJII is increased to 7.57 in the 24th month. The influence of DFHSI to DJII in the 1st month is 0.00. It influence is increased to 38.98 in the 24th month. The influence of DLNEXRATE to DJII in the 1st month is 0.00 and increased to 1.70 in 24th month. The influence of DCPI to DJII also 0.00 in the 1st month and it increased to 9.75 in the 24th month. The influence of DLNM2 to DJII in the 1st month is 0.00 and it increased to 1.16 in the 24th month. In the end, the influence of DBIR to DJII is 0.00 in the 1st month and it increased to 8.29 in the 24th month. DFHSI is the variable which has the biggest influence to DJII according to the percentage in the 24th month such as 38.98 respectively. ϲϭ

4.8 Economic Analysis on VECM Estimation Results

The economic analysis on VECM estimation results of this research will be divided into three sections based on the market categories, such as:

a. Capital Market

According to the result of VECM vector error correction model estimation above, we can analyze that domestic macroeconomics variables and global variables have significant effect to Indonesia capital market in short term and long term. The domestic macroeconomics variables have different effect to the islamic capital market in Indonesia, such as money supply has positive effect to Jakarta Islamic Index. It means that the movement of money supply inline with the Islamic capital market in Indonesia. The basis of positive effect from money supply is the increasing of corporate profit that will increasing the money supply. So, in the further it will increase the cash flow and will give impact to the increasing of stock prices. The increasing of stock prices will increasing the capital market index. Meanwhile, exchange rate, CPI and BI rate have negative effect to Jakart Islamic Index. It means that the movement of these three variables in contrast to the movement of capital market in Indonesia. The effect of Bank Indonesia interest rate happens because the increasing of BI rate will make the investors withdraw their money in capital market and investing in bank deposit which has minimum risk than capital market. The positive effect from the Dow Jones Islamic Market means that Indonesia capital market still following the global market condition. So, if the crash occurs abroad, then it may trigger a crash in Indonesia capital market. Beside that, the number of domestic investors are still lower than the foreign investors. It makes the money in domestic capital market can inflow and outflow to abroad whenever, so it will make our capital market