Factors Affecting the Volatility of the Jakarta Composite Index before and after the Merger of Two Stock and Bond Markets in Indonesia

Relations in economies and finance are often simplified in the form of models. The Jakarta Composite Index (JCI) has relations to some variables of gold price, SBI (The Central Bank’s Interest Rate), inflation, and GDP. The capital market in Indonesia has been growing to be a financial institution with strategic role in national economic development. Indonesia had ever had two capital markets: JSX (Jakarta Stock Exchange) and SSX (Surabaya Stock Exchange). Moreover, the two capital markets were merged to be BEI or IDX (Indonesia Stock Exchange) in 2008. With the merger, thus, there is only one capital market in Indonesia, i.e. BEI. The merger has implication to the management of capital market to stock trading liquidity and factors influencing the Indonesia Stock Exchange (IDX) Compose Price Index (IHSG). From the consideration, the research problem is how the impact of capital market merger to factors influencing the Indonesia Stock Exchange (IDX) Compose Price Index. The dissertation research has goal of analyzing effects of the gold price, SBI, inflation and GDP on JCI before and after the merger and formulating the consequences of the impact of the merger policy. The data of the variables used in this research are monthly time series for the period 2004(1) to 2012(12). Autocorrelation and heteroscedasticity problem persist in the initial model. To overcome these problems, the model was developed by using the ARCH/GARCH method. This model is expected to be useful to predict and make decisions related to volatility of the JCI and the affecting factors.


Research Questions
1) How do the gold price, SBI, inflation and GDP influence JCI before and after the merger of the two stocks and bond markets into ISX?
2) What are implications of the merger of JSX and SSX into ISX?

Research Objectives
1) Analyzing effects of the gold price, SBI, inflation and GDP on JCI before and after the merger of the two stocks and bond markets into ISX.
2) Formulating consequences of the merger policy of the two markets.

Data and Methodology
Data used in this study were time series, with the time period from 2004 to 2012 i.e. for nine years, where each year has twelve months, and as a whole it was amounted to 108 monthly observations.The monthly data were collected including JCI, the gold price, SBI, inflation, and the GDP.Sources of data were derived from financial statements of BEI, BI and BPS.Since the GDP is quarterly, this variable is made monthly by using a simple interpolation.

Figure 1. The study framework
The initial multiple regression models in this study is as follows where INFLASI is inflation, PDB is the GDP, EMAS is gold price, SBI is the Central Bank's Interest Rate, and µ t is the error term.
In the ARCH/GARCH methodology this relation can be written as follows: Where b i is regression coefficient of the i th independent variable (x it ).x it is the independent variable and e t is the error term.
In this research, the models formed include 1) the model before merger and 2) the model after the merger.

The Model before the Merger
This model is based on the monthly data of JCI, gold price, SBI, inflation and GDP for the period of 2004-2007, i.e. 48  It can be seen that all of the independent variables have significant effects on JCI because p-value smaller than α = 5%.The second step is to identify the existence of autocorrelation and heteroscedasticity problems by using the White tests.That is: the Obs*R-squared result = 4.61%, which is smaller than α = 5%, thus the data has autocorrelation and heteroscedasticity problems.Furthermore, the third step is to do test of six-phase ARCH/GARCH effects i.e.; ARCH (1), ARCH/GARCH (1,1), ARCH/GARCH (2,1), ARCH/GARCH (2,2), ARCH/GARCH (3,2), ARCH/GARCH (4,2).It is urgent because it solves the autocorrelation and heteroscedasticity problems.The method is to find the best model.Systematically the analysis is as follows:

ARCH (1)
After the analysis to the initial equation is carried out where the equation contains autocorrelation and heteroscedasticity problems, the next analysis is carried out to the ARCH/GARCH test.The first test is related to ARCH (1), and the results are as in Table 2.
From the above results, it is seen that the residual square elements are significant in the variance equation as shown in ARCH (1) with the results of RESID (-1)^2 which can be meant that the independent variables are very determinant toward JCI.

The Model after the Merger
In the situation "After the merger", the model is based on the period of 2008-2012, i.e. 60 months.As before, the autocorrelation and heteroscedasticity tests are performed, and then it is followed with the search of the best model through the ARCH/GARCH tests.
The first step is to search the initial regression among JCI and gold price, SBI, inflation, GDP, with OLS method, the initial multiple regression is as follows.than α = 5%.The second step is to identify the presence of autocorrelation and heteroscedasticity by using the White tests.The results are: Obs*R-squared = 0.37%, the value is smaller than α = 5% so the data has autocorrelation and heteroscedasticity problems.Furthermore, the next step is to do trial test of six-phase ARCH/GARCH effect i.e.ARCH (1), ARCH/GARCH (1,1), ARCH/GARCH (2,1), ARCH/GARCH (2,2), ARCH/GARCH (3,2), ARCH/GARCH (4,2).This is urgent in order to solve for the autocorrelation and heteroscedasticity problems.The method is to find the best model.Systematically the analysis is as follows:

ARCH (1)
The first test is carried out for the ARCH (1) model and the results are as follows in Table 5.
From the above results, it is seen that the residual square elements are not significant in the variance equation as shown in ARCH (1) generating RESID (-1)^2 p-value of 7.45%, which is tremendously greater than α = 5%, indicating the model does not contain autocorrelation and heteroscedaticity problems.Furthermore, R 2 is 74.27%; this value is great so it is interpreted that the independent variables are determinant toward JCI or INFLASI, PDB, HARGA EMAS, and SBI variables which can explain the JCI volatility.However, the ARCH (1) model cannot be enough to be the best model because there is one independent variable which is insignificant INFLASI.
The fourth step is to find the best model.After conducting the tests, by considering p-values and R 2 from each ARCH/GARCH, we get the best model, i.e.GARCH (4,2).The results are in Table 6 as follows.From the above table, it is seen that the residual square elements are significant in the variance equation as shown in ARCH(4) with the results of RESID (-1) ^2 p-value totaling 47.78%, which is tremendously greater than α = 5%, and GARCH(2) is zero percent.This suggests the absence of autocorrelation and heteroscedasticity problems.All independent variables in the model has p-value smaller than α = 5%.Furthermore, R 2 is 80.74%, the value is great so it can be interpreted that the independent variables are very determinant toward JCI or INFLASI, PDB, HARGA EMAS, and SBI variables can explain the JCI volatility.
The best model in Table 6 GARCH (4,2) is for "before the merger" can be written as follows: considering international indicators.In this case, the U.S. dollar exchange rate and international stocks like Don Jowns, Han Seng and others should be considered.The international indicators are very sensitive to the development of JCI.If the movement of JCI is not profitable due to the development of security and noneconomic activities, it will have an impact on the diversion of investment which moves toward capital flight.
Because this study did not include the international elements and focused more on domestic factors, the discussions of the research results illustrate the behaviors of domestic indicators.With the limitations in this analysis, it is expected that further researches incorporating elements of international indicators into the model will be conducted.
Frequently, foreign investors also pay attention to service organizations, the merger of JSX and SSX into ISX, ISX has to pay higher costs due to the construction of the information network of Jakarta Automatic Trading System (JATS).This results in higher fees for the transactions of stocks in ISX.When compared to the international capital markets such as in India, the transaction fees in ISX are much higher, and this can be a trigger which causes foreign investors to be less interested in investing their funds in Indonesia.

Conclusion and Recommendations
Based on the results of this research, it is concluded that the influence of INFLASI, PDB, HARGA EMAS and SBI variables to JCI is very significant both before and after the merger.However, in the analysis it is found that the influence of INFLASI is less significant to JCI before the merger.On the other hand, PDB after merger is less significant to JCI as compared to before the merger.After the merger, JCI tended to decrease significantly if compared to that before the merger.From the discussion, it is suggested that: 1) The regulators should be carrying out reviews to legislations and regulations on the administration of the capital market; so as to make it becomes more efficient.
2) The operators should improve and strengthen the organization of ISX and optimize JATS as well as to develop more stock exchange corner programs in each university campus.
3) Investors and potential investors should pay attention to the consequences of variables that have significant influences to JCI particularly by observing INFLASI, gold price, SBI, and GDP in the effort of optimizing fund allocation.
4) For any limitation of not using the international indicators, further researches should be carried out to analyze effects of international variables on JCI, and establish strategies for optimizing functions and roles of the ISX.
months as the research object before the merger of SSX and JSX is carried out.Moreover, the data are carried out through autocorrelation and heteroscedasticity tests.Moreover, it is followed with the search of the best model through the ARCH/GARCH test.
p-value of 38.05%, which is much greater than α = 5%, indicating the model does not contain the autocorrelation and heteroscedasticity problems.Furthermore, R 2 is 95.21%, a very large value, it can be interpreted that the independent variables are very determinant toward JCI or INFLASI, PDB, HARGA EMAS, and SBI variables which can explain the JCI phenomenon.However, the ARCH (1) model is not enough to be used as the best model because INFLASI does not significantly affecting JCI.Thus, the analysis is carried out in the next ARCH/GARCH test.
In the same way, the test is carried out to ARCH/GARCH effect, i.e.; ARCH/GARCH (1,1), GARCH (2,1), ARCH/GARCH (2,2), ARCH/GARCH (3,2) and ARCH/GARCH (4,2).The fourth step is to find the best model.After the trial tests are carried out to ARCH/GARCH by considering p-value and R 2 in each ARCH/GARCH, it finds the best model of GARCH (4,2).The result can be observed in Table3.From the above table, it is seen that the residual square elements are significant in the variance equation as shown by ARCH (4) generating RESID (-1)^2 p-value of 92.68%, and GARCH (2) 77.45%, which is greater than α = 5%, indicating the model has no autocorrelation and heteroscedasticity problems.All independent variables in the model are significant as they have p-values smaller than α = 5%.Furthermore, R 2 is 95.16%,

Table 4
It is seen that INFLASI and PDB variables are not significant affecting JCI where p-values are tremendously larger than α = 5%.The variables of HARGA EMAS and SBI are significant to JCI as the p-values are smaller