CHAPTER IV

PAGE 56

CHAPTER IVFINDINGS AND DISCUSSION

In this chapter will be described about results of data analysis which are tested by Augmented Dickey Fuller (ADF) unit root test, Johansen cointegration analysis, Granger causality analysis, and error correction model (ECM). This test will explore long-run relationship and short-run interdependence between stock markets and exchange rates during in the pre-Euro and the post-Euro periods. 4.1 Summary Statistics of the Stock Returns Table 4.1 Summary Statistics of the Stock Returns and Exchange RatePeriod:VariablesLQ45S&P500Nikkei225

Pre-Euro

PeriodMean-0.000141-0.0000897-0.000327

Maximum0.6397350.0488840.072217

Minimum-0.639869-0.060045-0.072340

Std. Dev.0.044580.0130370.015447

Skewness0.341996-0.00039-0.012546

Kurtosis133.84194.2355174.947074

Post-Euro

PeriodMean0.0013630.00006450.000075

Maximum0.3765630.057440.05732

Minimum-0.397078-0.042423-0.052258

Std. Dev.0.0403840.0120420.014162

Skewness-0.2446320.254982-0.134278

Kurtosis51.891074.97499993.566497

Table 4.1 provides summary statistics of the stock returns (stock prices in first difference) for the Indonesia, US, and Japan. It is interesting to note that during the pre-Euro, all stock markets recorded a negative average daily returns. Conversely, during the post-Euro, all stock markets recorded a positive average daily returns. During both of periods, the Indonesia stock market earned the highest average daily returns of 0.0752%, followed by US (0.0077%), and Japan (0.02%). Additionally, the finding that the Indonesian market had the highest returns in the region conforms to the theory of finance, which says that the riskier (more volatile) the market, the higher would be the returns. This evidence is supported by the standard deviation, where the Indonesian stock market recorded the highest, i.e., 0.04. While the standard deviations from the US and Japan were below 0.015. 4.2 Correlation of the Stock Returns and Exchange Rates during Pre- and Post-Euro Periods

To highlight the short run relations between the movements of the stock markets, the standard correlation coefficients are reported in Table 4.2. It is used to measure the extent of the association between the stock markets. During pre- and post-Euro periods, all 6 correlations pairs are found to be significantly correlated, at least at the 1% significant levels. During Pre-Euro period, Indonesia was recorded to have the highest correlation in the stock return with US who are represented by S&P500 Index. While the Nikkei225 Index has positive correlation coefficients with Indonesia market who is represented by LQ45 Index. But during the post-Euro period, Indonesia market has increased negative correlation coefficients with the US and Japan markets. And Indonesia was recorded to have highest correlation in the stock return with Japan market. It shows that new currency Euro has influenced the Indonesia stock market to decrease lower correlation with the US market. Table 4.2: Correlation of the Stock Returns Pre-Euro

Post-Euro

S&P500Nikkei225

LQ45-0.012526 *(0.0012)0.004909 *(0.0057)

S&P500Nikkei225

LQ45-0.031517 *(0.0038)-0.05276 *(0.0062)

Note: the ( ) show the p-value and * denotes variables significance at the 1% level.

To highlight short run relations between the movement of LQ45 Index and Exchange rates which are represented by US Dollar and Japan Yen are reported in Table 4.3. During pre- and post-Euro periods, all 6 correlations pairs are found to be significantly correlated, at least at the 1% significant levels. During pre-Euro period, Indonesia market has the highest correlation and has negative correlation coefficient with the US Dollar. Whereas during post-Euro period, Indonesia market has increased negative correlation coefficient with US Dollar and Japan Yen. And Indonesia has the highest correlation with US Dollar. The significance increase in the correlation coefficients in the Indonesia market indicates that there are short term co-movement between the markets and foreign exchange rates, which suggests that the benefits of any short term diversification, or speculative activities, are limited within the region.

Table 4.3: Correlation of the Indonesia Market and Exchange Rates

Pre-Euro

Post-Euro

US DollarJapan Yen

LQ45-0.04805 *(0.0053)-0.04723 *(0.0051)

US DollarJapan Yen

LQ45-0.12605 *(0.0098)-0.06414 *(0.0074)

Note: the ( ) show the p-value and * denotes variables significance at the 1% level4.3 Tests of the Unit Roots HypothesisIn order to obtain credible and robust results for any conventional regression analysis, the data to be analyzed should be stationary (Gujarati, 1995). Hence, to test for stationarity, the ADF tests are performed based on model with constant and trend. Table 4.4 reports the ADF tests statistics that examine the presence of unit roots (non-stationary) for all stock indices and exchange rates in pre-Euro period.

Table 4.4: Unit Root Test in Pre-Euro Period

VariableLevelCritical ValueFirst-DifferenceCritical Value

LQ45 -1.682966-3.438842-37.52432 *-3.438842

S&P500-1.800147-3.438842-13.41551 *-3.438842

Nikkei225-0.529673-3.438842-28.14686 *-3.438842

US Dollar-1.198937-3.438842-20.53392 *-3.438842

Japan Yen-1.907079-3.438842-20.86645 *-3.438842

Note: * denotes significance at the 1% level. The lag lengths included in the models are based on the Akaike Information Criteria (AIC). The above test of ADF (Augmented Dickey-Fuller) is based on model with constant and trend. The study finds that all stock indices and exchange rates contain a unit root, implying that the null- hypothesis of the presence of a unit root at level cannot be rejected even at the 1% significance level. Since the indices and exchange rates are found to be non-stationary at levels, the first differences for whole models are taken. The same tests are applied to the first differences of the indices and the results show that all the indices become stationary after differencing once. This result indicates that all index levels and exchange rates are integrated of order one, I(1) and, therefore, it can proceed to the cointegration analysis with these indices because they are all integrated in the same order as required for cointegration, Granger causality, Error Correction model (ECM) and Generalized Method of Moments (GMM). For our present analysis, this, therefore, serves as a prerequisite for our empirical models.

Table 4.5 reports the ADF tests statistics that examine the presence of unit roots (non-stationary) for all stock indices and exchange rates in post-Euro period.

Table 4.5: Unit Root Test in Post-Euro Period

VariableLevelCritical ValueFirst-DifferenceCritical Value

LQ45 0.537002-3.438842-6.209546 *-3.438842

S&P500-1.187423-3.438842-28.70595 *-3.438842

Nikkei225-1.526317-3.438842-26.77742 *-3.438842

US Dollar-2.848919-3.438842-24.45507 *-3.438842

Japan Yen-0.196957-3.438842-22.76739 *-3.438842

Note: * denotes significance at the 1% level. The lag lengths included in the models are based on the Akaike Information Criteria (AIC). The above test of ADF (Augmented Dickey-Fuller) is based on model with constant and trend. The study finds that all stock indices and exchange rates contain a unit root, implying that the null- hypothesis of the presence of a unit root at level cannot be rejected even at the 1% significance level. Since the indices and exchange rates are found to be non-stationary at levels, the first differences for whole models are taken. This result indicates that all index levels and exchange rates are integrated of order one, I(1) and, therefore, we can proceed to the cointegration analysis with these indices because they are all integrated in the same order as required for cointegration, Granger causality, and Error Correction model (ECM). For our present analysis, this, therefore, serves as a prerequisite for our empirical models. Having identified that all stock markets and exchange rates are stationary at first difference, it can proceed to test for cointegration, aiming at investigating whether there exist long run and short run between stock markets and exchange rates. 4.4 Long-Run AnalysisAfter all of stock markets and exchange rates are stationary at first difference, the next step is to estimate the appropriate cointegrating vector using LQ45 index, S&P500 index, Nkkei225 index, US Dollar, and Japan Yen data. This analysis will be divided into two parts that are cointegrating among stock markets and cointegrating between stock markets and exchange rates.

4.4.1 Long-run Relationship between the Indonesia and the US MarketsThe analysis is aiming at investigating whether there exist a long run relationship between the Indonesia and the US stock markets in the pre- and post-Euro period. The next step is to estimate the appropriate cointegrating vector using LQ45, and S&P500 indices data as follows:LQ45 =

(4.1)

Note that all series are in natural logarithms. This data has a daily frequency and running from January 1999 until December 2001 for pre-Euro period and from January 2002 until December 2004 for post-Euro period. All of data time series show strong stationary. Table 4.6: Cointegration Regression between the Indonesia and US marketPeriodOLS Regression Equation

Pre-Euro

(-10.494 *) (13.533 *)

Post-Euro

(-16.97 *) (32.381 *)

Note: the number ( ) show the t-statistic value and * denotes variables significance at the 1% level.Table 4.6.1: Johansen Cointegration Test between the Indonesia and US market

Variables

CombinationNull HypothesisPre-Euro PeriodPost-Euro Period

TSMESTSMES

LQ45 and S&Pr 036.158 *26.72 *41.562 *29.32 *

Note: * denote significance at 1% levels. r denotes the number of cointegrating vectors. TS and MES refer to Trace Statistic and Max-Eigen Statistic tests, respectively.

From table 4.6, reports the cointegrating regression results between the Indonesia and the US stock markets where the t-statistic values show the independent variables significance at the 1% level during in both the periods. Whereas table 4.6.1 reports the Johansen cointegrating test that the both variables are significant at the 1% level during in both the periods. All of test employ interval lag from 1 to 4. During in both the periods, the Indonesia market has a significantly positive relationship with the US market. This indicates that during in pre-Euro running to post-Euro periods, the Indonesia stock market has maintained a long-run relationship with the US market. Even though the influence weight of US stock market has gradually decreased toward the Indonesia stock market in the long-term. It can be looked from the table 4.6 shows that the slope of coefficient variable S&P500 sharply increases after the introduction of Euro.

Some general conclusions that can be drawn from this finding is that the Indonesia stock market is moving towards a greater integration in both the periods with the US market. This finding is consistent with many previous findings that documented the world capital markets have been increasingly integrated and Indonesia has a long run relationship with advanced markets whose are the US (Majid et al., 2007; Dekker et al., 2001). It implies an increase in US imports and an increase in Indonesia country exports that create substantial trade links between Indonesia and US countries. The possible reason is because of financial deregulation adopted by these countries to recover from the crisis. The financial liberalization has increased integration both locally and internationally, but has not drive up local market volatility. The other reasons are due to distribution of inward foreign direct investment flow, and the strength of trade between the two economies. 4.4.2 Long-Run Relationship between the Indonesia and the Japan Markets

The analysis is aiming at investigating whether there exist a long run relationship between the Indonesia and the Japan stock markets in the pre- and post-Euro period. The next step is to estimate the appropriate cointegrating vector using LQ45, and Nikkei225 indices data as follows:

LQ45 =

(4.2)

Note that all series are in natural logarithms. This data has a daily frequency and running from January 1999 until December 2001 for pre-Euro period and from January 2002 until December 2004 for post-Euro period. All of data time series show strong stationary.

Table 4.7: Cointegrating Regression between The Indonesia and Japan marketPeriodOLS Regression Equation

Pre-Euro

(-2.894 *) (29.466 *)

Post-Euro

(-10.62 *) (25.914 *)

Note: the number ( ) show the t-statistic value and * denotes variables significance at the 1% level.Table 4.7.1: Johansen Cointegration Test between the Indonesia and Japan market

Variables

CombinationNull HypothesisPre-Euro PeriodPost-Euro Period

TSMESTSMES

LQ45 and Nikkeir 034.227 *25.79 *38.145 *27.62 *

Note: * denote significance at 1% levels. r denotes the number of cointegrating vectors. TS and MES refer to Trace Statistic and Max-Eigen Statistic tests, respectively.

From table 4.7, reports the cointegrating regression results between the Indonesia and the Japan stock markets where the t-statistic values show the independent variables significance at the 1% level during in both the periods. Whereas table 4.7.1 reports the Johansen cointegrating test that the both variables are significant at the 1% level during in both the periods. All of test employ interval lag from 1 to 4. During in the pre-Euro period, the Indonesia market has a significantly positive relationship with the Japan market. While during in the post-euro period, the Indonesia market has a significantly positive relationship with the Japan market. This indicates that during in pre-Euro running to post-Euro periods, the Indonesia stock market has maintained a long-run relationship with the US market. Even though the influence weight of Japan stock market has sharply increased toward the Indonesia stock market in the long-term. It can be looked from the table 4.7 shows that the slope of coefficient variable Nikkei225 increases after the introduction of Euro.

Some general conclusions that can be drawn from this finding is that the Indonesia stock market is moving towards a greater integration in both the periods with the Japan market. This finding is consistent with many previous findings that documented the ASEAN and Central Asia markets have been increasingly integrated toward the Japan market (Dunis and Shannon, 2005). With an integrated the Indonesia stock market, investors from the Japan country will be able to allocate capital to the locations in the region where it is the most productive. With more cross-border flows of funds, additional trading in individual securities will improve the liquidity of the stock markets, which will turn lower the cost of capital for firms seeking capital and lower the transaction costs investor incur. Besides foreign investors, many large Indonesia companies are cross-listed on Tokyo Stock Exchange or have the Tokyo Stock Exchange as their primary listing. Thus, an integrated stock market within the Indonesia will help link the region with the Japan stock market and bring more capital into the country from abroad. This will allow the Indonesia companies to expand their shareholder base and lower their cost of capital even further. So foreign investors may value the benefits of stock market because of higher liquidity and lower transaction costs. 4.4.3 Long-run Relationship between the Indonesia Market and the US DollarThe analysis is aiming at investigating whether there exist a long run relationship between the Indonesia market and the US Dollar currency in the pre- and post-Euro period. The next step is to estimate the appropriate cointegrating vector using LQ45 index, and the US Dollar currency data as follows:

LQ45 =

(4.3)

Note that all series are in natural logarithms. This data has a daily frequency and running from January 1999 until December 2001 for pre-Euro period and from January 2002 until December 2004 for post-Euro period. All of data time series show strong stationary.

Table 4.8: Cointegrating Regression between the Indonesia and US DollarPeriodRegression Equation

Pre-Euro

(-8.834 *) (-40.45*)

Post-Euro

(-9.839 *) (-38.58*)

Note: the number ( ) show the t-statistic value and * denotes variables significance at the 1% level.

Table 4.8.1: Johansen Cointegration Test between the Indonesia and US DollarVariables

CombinationNull HypothesisPre-Euro PeriodPost-Euro Period

TSMESTSMES

LQ45 and US$r 018.04 **16.03 **15.57 **14.54 **

Note: ** denote significance at 5% levels. r denotes the number of cointegrating vectors. TS and MES refer to Trace Statistic and Max-Eigen Statistic tests, respectively. From table 4.8, reports the cointegrating regression results between the Indonesia market and the US Dollar currency where the t-statistic values show the independent variables significance at the 1% level during in both the periods. Whereas table 4.8.1 reports the Johansen cointegrating test that the both variables are significant at the 5% level during in both the periods. All of test employ interval lag from 1 to 10. During in both the periods, the Indonesia market has a significantly negative relationship with the US Dollar currency. This indicates that during in pre-Euro running to post-Euro periods, the Indonesia stock market has maintained a long-run relationship with the US Dollar currency. This result is surprised that the influence weight of US Dollar currency has gradually decreased toward the Indonesia stock market in the long-run after the introduction of Euro. It can be looked from the table 4.8 shows that the slope of coefficient variable USD decreases after the introduction of Euro.

Some general conclusions that can be drawn from this finding is that the Indonesia stock market is negatively influenced by the US Dollar during in both the periods. This finding is in line with many previous findings that documented the emerging capital markets have a long run relationship with exchange rates whose are US Dollar currency (Ajayi and Mongoue, 1996). There are two possible reasons. Firstly in the pre- and post-Euro period, appreciating US Dollar to Rupiah will decrease the price of Indonesia market because the foreign investors will sell their domestic assets, and buy their local currency. Directly, this impact will make demand of foreign currency sharply increases. Secondly, the companies are listed in LQ45 Index where they have burden debts denominated in US Dollar. If the US Dollar is strong, it will decrease the performance cash flow of local company.4.4.4 Long-run Relationship between the Indonesia Market and the Japan YenThe analysis is aiming at investigating whether there exist a long run relationship between the Indonesia market and the Japan Yen currency in the pre- and post-Euro period. The next step is to estimate the appropriate cointegrating vector using LQ45 index, and Japan Yen currency data as follows:

LQ45 =

(4.4)

Note that all series are in natural logarithms. This data has a daily frequency and running from January 1999 until December 2001 for pre-Euro period and from January 2002 until December 2004 for post-Euro period. All of data time series show strong stationary.

Table 4.9: Cointegrating Regression between the Indonesia Market and YenPeriodOLS Regression Equation

Pre-Euro

(26.62 *) (-41.9 *)

Post-Euro

(-24.57 *) (33.91 *)

Note: the number ( ) show the t-statistic value and * denotes variables significance at the 1% level.Table 4.9.1: Johansen Cointegration Test between the Indonesia and Japan YenVariables

CombinationNull HypothesisPre-Euro PeriodPost-Euro Period

TSMESTSMES

LQ45 and US$r 017.63 **16.37 **15.94 **14.24 **

Note: ** denote significance at 5% levels. r denotes the number of cointegrating vectors. TS and MES refer to Trace Statistic and Max-Eigen Statistic tests, respectively. From table 4.9, reports the cointegrating regression results between the Indonesia market and the Japan Yen currency where the t-statistic values show the independent variables significance at the 1% level during in both the periods. Whereas table 4.9.1 reports the Johansen cointegrating test that the both variables are significant at the 5% level during in both the periods. All of test employ interval lag from 1 to 10. During in the pre-Euro period, the Indonesia market has a significantly negative relationship with the Japan Yen currency. While during in the post-Euro period, this result is reversal from pre-Euro. This indicates that during in pre-Euro running to post-Euro periods, the Indonesia stock market has maintained a long-run relationship with the Japan Yen currency. This result is surprised that the influence weight of Japan Yen currency has drastically increased toward the Indonesia stock market in the long-run after the introduction of Euro. It can be looked from the table 4.9 shows that the slope of coefficient variable YEN increases after the introduction of Euro.

Some general conclusions that can be drawn from this finding is that the Indonesia stock market is negatively influenced by the Japan Yen during in both the periods. This finding is in line with many previous findings that documented the emerging capital markets have a stable long run relationship with exchange rates whose are Japan Yen currency (Yu Qiao, 1997). There are two possible reasons. Firstly in the pre- and post-Euro period, appreciating Japan Yen to Rupiah will increase the price share of Indonesia market because the local firms become more competitive, leading to an increase in their exports. Directly, this impact will raise cash flow of local company. Secondly, many companies are listed in LQ45 Index which is subsidiaries firm from Japan. So, the positive impact for Japan subsidiary companies which are established in the Indonesia can decrease their operational cost because of depreciated Rupiah to Yen. It influences the increases net income of the Japan subsidiaries company. 4.5 Short Run AnalysisIn this part will examine interactions among Indonesia market, abroad stock markets (the US and Japan) and exchange rates (US Dollar and Yen) whether there is short run and causality between two variables during pre- and post-Euro periods. Applying Granger causality to test if two variables have bidirectional or unidirectional causalities running and applying Error Correction Model (ECM) to get a coefficient variable in short term. Correlation techniques do not directly address the question of causality, but the correlations should be able to shed light on the opportunities for investors to diversify across these markets. 4.5.1 Short-run Interdependence between the Indonesia and US MarketsThe analysis is aiming at investigating whether there exist a short-run between the Indonesia and the US stock markets which are represented LQ45 and S&P500 Indices respectively during in the pre- and post-Euro period. After getting Granger causality between Indonesia market and advanced markets which are the US market, the next step how to determine coefficient variable of regression equation in short term by using two step Engle-Granger (EG). With the error correction model equation, the regression result should be appropriate with results of Granger causality. In the first regression, it will just take only the residual value. After getting the residual value, it will be regressed gathers with the second equation for obtaining the significant results from independent variables. If the statistic result of residual value is significant, it shows that has short term in its equation.

Note that all series are in natural logarithms. This data has a daily frequency and running from January 1999 until December 2001 for pre-Euro period and from January 2002 until December 2004 for post-Euro period. All of data time series show strong stationary.

Table 4.10: Bivariate Granger Causality between Indonesia and US markets

LagPre-Euro PeriodLagPost-Euro Period

1 to 4Indonesia or or USD(4.58 *) (0.98) 1 to 4LQ45 USD

(2.01 ***) (3.08 **)

Note: indicates a bidirectional Granger causality between the stock markets; ==> or YEN

(3.22 **) (1.12) 1 to 4LQ45 or