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\title{Effect of Market Risk Premium and Exchange Rate on the Return of Jakarta Islamic Index}
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             \author[1]{  Fatmawati}

             \author[2]{Hendri  Tanjung}

             \author[3]{  Endri}

             \affil[1]{  Ibn Khaldun Bogor University, Indonesia.}

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\date{\small \em Received: 9 November 2020 Accepted: 5 December 2020 Published: 15 December 2020}

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\begin{abstract}
        


Effect of Market Risk Premium and Exchange Rate on the Return of Jakarta IslamicEffect of Market Risk Premium and Exchange Rate on the Return of Jakarta Islamic Index. This study aims to analyze the effect of market risk premium and exchange rate on the return of Jakarta Islamic Index. By using the Vector Auto regression (VAR), this study utilizes time series monthly data from January 2008 to December 2018. This paper finds that the market risk premium has no significant effect on the return of Jakarta Islamic Index, while the exchange rate has a significant positive effect on the return of Jakarta Islamic Index. Shocks to market risk premium are responded negatively by return of Jakarta Islamic Index while shocks to exchange rate are responded positively by return of Jakarta Islamic Index

\end{abstract}


\keywords{market risk premium, exchange rate, return, jii, var.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
long with the development of the Islamic economy, sharia capital markets are also developing in various countries, including Indonesia. To meet the needs of the public for investments that avoid usury, in Indonesia began to develop the Islamic capital market, which provides an opportunity for investors who want to develop their investments according to Islamic principles. One of the existing index in the Indonesia Stock Exchange (IDX) is Jakarta Islamic Index (JII), which calculates the index average of 30 stocks that meet the criteria of sharia, the bigest market capitalization and having a high level of liquidity in trade values.\par
Jakarta Islamic Index (JII) was launched for the first time on July 3, 2000 and is evaluated every six months. From year to year, the Jakarta Islamic Index (JII) shows significant developments. This can be shown as in Figure \hyperref[fig_0]{1}.   {\ref 349}. In 2016 the index increased by 15.05\%, which was in the position of 694.127 and followed by an increase in 2017 at the level of 759.07 or up 9.36\%. At the end of 2018 JII decreased by 9.73\% to 685,223.\par
In line with the international economic turmoil, the capital market world also faces risks that must be faced by issuers and investors. In the capital markets, there is a risk in line with the fluctuations in prices on the market, which will bring the impact on the return stock . In addition, there is also the risk of the market which is measured by the beta as a risk which is faced by an instrument of investment that is caused by a factor of economic, social, political and so on. An investor who take risks more will get a return that is better. In Arbitrage Pricing Theory (APT), risk premium which is an additional level of return for investors taking the risk that more, will affect the rate of return. In this study, the variable used is market risk premium that describe general economic conditions. As for the measurement of market risk premium variable measured from the market return (R M ) minus the risk-free return of bank Indonesia (R F ) or market risk premium is R M -R F . Whereas R M is measured by the movement of the Composite Stock Price Index in the Indonesia Stock Exchange. R M -R F is the risk premium level of the market portfolio which is proportional to the risk of the risk aversion level of each investor.\par
One of the macroeconomic factors that affect the return is exchange rate.\par
Exchange rate is the price of currency against the currency of money more, where the prices are related to the offer and demand of money. Exchange rate also often fluctuate in accordance with the situation and economic conditions in a country. Rupiah exchange rate against the US\$ greatly affects stability of the stock price in the market, especially for issuers who use US\$ in their international transactions, so when the exchange rate of Rupiah decreased, then Composite Stock Price Index also declined .\par
According to \hyperref[b28]{Tanjung (2014)} the Jakarta Islamic Index return fluctuate greatly. Observations using JII daily data from 2 March 2009 to 30 September 2013 (1122 observations) show that JII return do not spread normally. The minimum return is almost-10\% and the maximum is almost 10\%, this shows that in one day, there is a possibility to get a loss of almost 10\% and a profit of almost 10\%.\par
Jakarta Islamic Index observations for period 2 March 2009 -28 July 2009 or with 100 data (4 months) show that the distribution of JII's return is normal, while observations with 300 data (1 year) show that the distribution of JII's return is not normal, observations with 600 data (2 years) shows the distribution of JII's return data is not normal and observations with 900 data (3 years) indicate that the distribution of JII's return is not normal \hyperref[b28]{(Tanjung, 2014)}.\par
By the same analysis technique and enlarged data from 2008 to 2018, JII's return also looks very fluctuating, as can be seen in Figure 2. In this graph shows that the return of the Jakarta Islamic Index minimum of almost -15 \% and a maximum of nearly 10\%, which shows that in one day, there is the possibility to get loss nearly 15 \% and profit nearly 10 \% . The distribution of JII's return data from 2008 to 2018 can be seen in Figure 3. From the graph it can be known that Anderson Darling Test of 31, 677 and P-Value < 0.005. This means that the zero hypothesis which states that the distribution of JII return normally rejected, regard this indicates that the distribution of JII return from 2008 to 2018 does not spread to normal. So, JII return only partially minor who has a normal distribution, ie the data small. Volatility of JII is smaller by small data and bigger by large data. \hyperref[b28]{(Tanjung, 2014)} Based on the Jakarta Islamic Index return which is very fluctuating and most of the data are not normal, this study aims to analyze the effect of market risk premium and exchange rate on the Jakarta Islamic Index return. Based on the results of these studies indicate that the JII return is highly fluctuating and does not spread normally \hyperref[b28]{(Tanjung, 2014)}. The study of  {\ref Rjoub, Tursoy and Gunsel (2009)} showed that risk premium has a significant effect on return. Research of \hyperref[b14]{Mayfi and Rudianto (2014)} and \hyperref[b30]{Utami and Herlambang (2016)} show that the exchange rate of significant positive effect on JII. While Muhayatsyah (2012), \hyperref[b2]{Dewanti (2013)} and \hyperref[b18]{Pantas (2017)} found that the exchange rate has a significant negative effect on JII. \hyperref[b13]{Leong and Hui (2014)} revealed that exchange rate has a negative relationship on return. \hyperref[b25]{Sudarsono (2018)} found that the exchange rate has a negative effect on ISSI. Febrina, Sumiati and Ratnawati (2018) revealed that the exchange rate had a negative effect on the Composite Index. \hyperref[b17]{Omoruyi and Osaretin (2015)} found that the exchange rate has a negative relationship with the stock market index in Nigeria. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
Hsing's research  {\ref (2008)} shows that exchange rates are positively influenced by stock prices. \hyperref[b11]{Jorion (1990)} found that only 15 companies have exchange rate significantly from 287 company tested, \hyperref[b1]{Bodnar and Gentry (1993)} found that the exchange rate significantly in 22 of the 78 industries examined, Salifu, Osei and Adjasi (2007) found that out of 20 companies, 11 companies have significant exposure to USD exchange rate risk. Jayashankar and Ruth (2017) revealed that there is a reciprocal relationship between stock prices, exchange rates and interest rates in India. Jiranyakul (2012) found a positive directional causality relationship from stock market returns to exchange rates in Thailand. \hyperref[b12]{Khan and Khan (2018)} show that exchange rates and interest rates have a significant effect on share prices on the Karachi Stock Exchange in the long run.\par
The research of Beik and Fatmawati (2014) shows that the exchange rate has no significant effect on the Jakarta Islamic Index. Naik and Padhi (2012) find that exchange rates and short-term interest rates are not significant in determining stock prices. 
\section[{III.}]{III.} 
\section[{Hypothesis}]{Hypothesis}\par
This research was conducted to answer various questions contained in previous literature by using the following hypotheses: 1. The effect of market risk premium on the Jakarta Islamic Index return Ho: Market risk premium has no effect on the return of Jakarta Islamic Index. H1: Market risk premium influences the return of Jakarta Islamic Index. 2. The effect of exchange rate on the return of Jakarta Islamic Index Ho: The exchange rate has no effect on the return of Jakarta Islamic Index. H1: Exchange rates affect the Jakarta Islamic Index return. 
\section[{IV. Method}]{IV. Method}\par
This study analyzes the effect of market risk premium and exchange rate on the Jakarta Islamic Index return. The models in this study are as follows:RJII = ? 0 + ? 1 (R M -R F ) + ? 2 ER + e\par
Where: RJII = Return of the Jakarta Islamic Index? 0 = Interception ? 1 , ? 2 = Parameters R M -R F = Market Risk Premium ER = IDR/US\$ exchange rate e = Error\par
The variables can be defined operasionally as shown in table \hyperref[tab_0]{1}. This study uses descriptive analysis method and quantitative. The analytical tool used is the Vector Auto regression (VAR) method. The general VAR equation is as follows \hyperref[b5]{(Firdaus 2011}):y t = A 0 + A 1 y t-1 + A 2 y t-2 + ... + A p y t-p + ? t\par
Where: y t = size vector (n.1) containing n variables contained in a VAR model A 0 = intercept vector sized (n.1) A i = coefficient matrix / size parameter (nn) for each i = 1,2, ..., p ? t = error vector sized (n.1) The stages of analysis using VAR model are as follows: 
\section[{a) Data Stationarity Test}]{a) Data Stationarity Test}\par
The first step in estimating the VAR model is the data stationarity test. The selected data is stationary data that does not contain unit roots, because if the data used contain unit root elements, it will be difficult to estimate a model because the trend of the data tends to fluctuate around the average value \hyperref[b7]{(Gujarati, 2004 )} . So it is necessary to test the stationarity of data using the Augmented Dickey-Fuller (ADF). If the value of the ADF statistic is smaller than MacKinnon Critical Value, it can be concluded that the data is stationary. 
\section[{b) Optimum Lag Test}]{b) Optimum Lag Test}\par
This test is carried out to form a good VAR model by determining the optimum lag length. Determination of the number of lags that will be used in the VAR model can be determined based on the criteria of Akaike Information Criterion (AIC), Schwarz Information Criterion (SC) or Hannan Quinon Criterion (HQ). The lag that will be selected in this research model is the model with the smallest value. Too much lag length increases degrees of freedom, so smaller lags are recommended to minimize error specifications \hyperref[b7]{(Gujarati, 2004 )}. 
\section[{c) VAR Stability Test}]{c) VAR Stability Test}\par
VAR stability test is done by calculating the roots of polynomial functions or known as roots of characteristic polynomials. If all the roots of the polynomial function are inside the unit circle, the VAR model is considered stable so that the Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) produced are considered valid \hyperref[b5]{(Firdaus, 2011)}. Impulse Response Function (IRF) is a method used to determine the response of an endogenous variable to a particular shock. This is because shock variables, for example the i-th variable, do not only affect the i-th variable but are transmitted to all other endogenous variables through dynamic structures or lag structures in VAR. In other words, IRF measures the impact of a shock at a time on the innovation of endogenous variables at that time and in the future. IRF aims to isolate shocks to be more specific, which means that a variable can be affected by certain shocks or shocks. If a variable cannot be affected by shock, then the specific shock cannot be known but rather shock in general \hyperref[b5]{(Firdaus, 2011}). 
\section[{e) Forecast Error Variance Decomposition (FEVD)}]{e) Forecast Error Variance Decomposition (FEVD)}\par
The FEVD analysis in the VAR model aims to predict the contribution of the percentage of variance s of each variable due to changes in certain variables in the VAR system. This test provides information about the proportion of the movement of the effect of shock on one variable against other variables at this time and future periods. Then it can be seen how strong the composition of the role of variables on other variables and it can also be known which shock variables have the most important role in explaining changes in other variables in the study period \hyperref[b29]{(Tanjung and Devi, 2018)}.\par
V. 
\section[{Discussion a) Data Stationarity Test Results}]{Discussion a) Data Stationarity Test Results}\par
The first step in analyzing time series data is the data stationarity test. Unit root test results at the level shown in table \hyperref[tab_1]{2}.  
\section[{c) Stability Test Results}]{c) Stability Test Results}\par
The VAR model is considered stable if the modulus value for all roots is smaller than one and is at an optimal point. The results of stability test can be seen in table \hyperref[tab_3]{4} .  
\section[{Global Journal of Management and Business}]{Global Journal of Management and Business} 
\section[{d) VAR estimation Results}]{d) VAR estimation Results}\par
premium (MRP) does not significantly influence on the return of Jakarta Islamic Index (RJII), while the exchange rate (ER) has a significant positive effect on the return of the Jakarta Islamic Index . The effect of independent variables on the dependent variable can be analyzed as follows:\par
a. Hypothesis Testing 1\par
The first hypothesis proposed states that the market risk premium does not affect on the Jakarta Islamic Index return. Based on the results of the study, in the first lag the market risk premium has a tcount value of 0. 54029 with a regression coefficient of 0.187815. Because of the t arithmetic is smaller than 2 then Ho is accepted and H1 is rejected which means that the market risk premium has no significant effect on the return of Jakarta Islamic Index.\par
This study result contradicts with APT theory developed by \hyperref[b21]{Ross (1976)} which states that stock returns are affected by the risk premium. The results of research that reinforce the concept of this theory are research conducted by Rjoub, Tursoy and Gunsel (2009) which shows that the risk premium has a significant influence on the stock return.\par
This difference occurs because of the Jakarta Islamic Index shares are based on sharia principles which avoid the usury/interest element. Because the market risk premium is a picture of general economic conditions in which many conventional stocks still apply not in accordance with sharia. So the ups and downs of the market risk premium (general economic conditions) will not provide significant impact for investors, so investors are more secure to invest in stocks of Jakarta Islamic Index as compared to conventional shares. 
\section[{b. Hypothesis Testing 2}]{b. Hypothesis Testing 2}\par
The second hypothesis states that the exchange rate has no effect on the return of the Jakarta Islamic Index. Based on the results of the study indicate that the exchange rate in the first lag has a regression coefficient of 0.573712 and t-arithmetic of 2.44328 . Because t arithmetic is greater than 2, then Ho is rejected and H1 is accepted which means that the exchange rate has a significant positive effect on the return of Jakarta Islamic Index. This is consistent with the Portfolio Balance Model (PBM) approach from \hyperref[b6]{Frankel (1983)} which states that there is a positive relationship between exchange rates and stock returns. The results of the study that reinforce the concept of this theory are research conducted by \hyperref[b14]{Mayfi and Rudianto (2014)} and \hyperref[b30]{Utami and Herlambang (2016)} which show that the exchange rate has a significant positive effect on JII.\par
But the results of this study differ from the concept of Solnik's theory (1987) which states that the depreciation of the exchange rate will increase stock prices. The research that supporting this theory is the research conducted by Muhayatsyah (2012), Dewanti (2013) and \hyperref[b18]{Pantas (2017)} which shows that the exchange rate has a significant negative effect on JII. Leong and Hui (2014) revealed that exchange rates have a negative relationship with stock returns. \hyperref[b25]{Sudarsono (2018)} found that the exchange rate had a negative effect on ISSI. Febrina, Sumiati and Ratnawati (2018) found that the exchange rate had a negative effect on the Composite Index . \hyperref[b17]{Omoruyi and Osaretin (2015)} found that the exchange rate has a negative relationship with the stock market index in Nigeria.\par
Hsing  {\ref 's research (2008)} shows that exchange rates are positively influenced by stock prices. \hyperref[b11]{Jorion (1990)} found that only 15 companies have exchange rate significantly from 287 company tested, \hyperref[b1]{Bodnar and Gentry (1993)} found that the exchange rate significantly in 22 of the 78 industries examined, \hyperref[b22]{Salifu, Osei and Adjasi (2007)} found that out of 20 companies, 11 companies have significant exposure to USD exchange rate risk. Jayashankar and Ruth (2017) revealed that there is a reciprocal relationship between stock prices, exchange rates and interest rates in India. \hyperref[b10]{Jiranyakul (2012)} found a positive directional causality relationship from stock market returns to exchange rates in Thailand. \hyperref[b12]{Khan and Khan (2018)} show that exchange rates and interest rates have a significant effect on share prices on the Karachi Stock Exchange in the long run.\par
The research of \hyperref[b0]{Beik and Fatmawati (2014)} shows that the exchange rate has no significant effect on the Jakarta Islamic Index. Naik and Padhi (2012) find that exchange rates and short-term interest rates are not significant in determining stock prices.\par
The significant effect of the exchange rate on the return of Jakarta Islamic Index indicates that the number of foreign investors investing in Jakarta Islamic Index shares is quite large. This shows a good investment climate for Islamic stocks in Indonesia, because it has the trust of foreign parties, then investors need to pay attention to this factor. 
\section[{e) Impulse Response Function ( IRF )}]{e) Impulse Response Function ( IRF )}\par
The results of the Impulse Response Function for the Jakarta Islamic Index return are shown in Figure 4 . The figure shows RJII's response to shocks in the MRP and ER fluctuates. In the event of shocks to the market risk premium (MRP) then JII's return (RJII) will respond negatively or decreased in the second period of 0.000977, then fluctuations occur until the fifth period of 3.30E-06 before achieve stability in the sixth period until the end of the period. If there is a shock to the exchange rate, the Jakarta Islamic Index returns will respond positively in the second period of 0.012158 , then fluctuations occur until the eighth period of 7.09E-07, and achieve stability in the ninth period to the 132nd period. 
\section[{Global}]{Global} 
\section[{Global}]{Global} 
\section[{f) Forecast Error Variance Decomposition (F EVD)}]{f) Forecast Error Variance Decomposition (F EVD)}\par
Forecast Error Variance Decomposition analysis results as shown in Figure 5.  The figure 5 shows that in the first period, the contribution to variability of the return of Jakarta Islamic Index (RJII) 100\% came from RJII itself. This percentage decreased gradually until the end of the period, but it was still very dominant, amounting to 95.91 percent in the 132nd month.\par
The second variable that contributes to RJII diversity is the exchange rate (ER), it emerged start of the second period. ER contributed to RJII diversity in the second period by 3.89 percent and slowly increased until the 132nd period. ER variable can explain RJII variability with a contribution of 3.95 percent at the end of the period.\par
The third variable that contributes to RJII diversity is Market Risk Premium (MRP). The contribution of this variable begins to appear in the second period. MRP can explain RJII variability with a contribution of 0.03 percent in the second period and slowly rises until the end of the period. MRP can explain RJII variability of 0.14 percent in the 132nd period. 
\section[{VI.}]{VI.} 
\section[{Conclusion}]{Conclusion}\par
Based on the results of the analysis and testing of hypotheses that have been done about the effect of market risk premium and exchange rate on the return of Jakarta Islamic Index (JII) using the Vector Auto regression (VAR) method, it can be concluded that: 1. Market risk premium has no significant effect on the return of Jakarta Islamic Index in the period 2008 to 2018.\par
2. Rupiah exchange rate against USD affect positively significant on the return of Jakarta Islamic Index period 2008-2018. 3. Based on the result of the Impulse Response Function (IRF), it shows that if there is a shock at the market risk premium, JII's return will respond negatively and begin to stabilize in the 6th period until the end of the period. As for if there is a shock to the exchange rate, JII's return will respond positively and begin to stabilize in the 9th period until the end of the period. 4. The result of Forecast Error Variance Decomposition (FEVD) shows that the fluctuation in the return of Jakarta Islamic Index most dominant influenced by JII's return itself, followed by the exchange rate in the second and the market risk premium on the third. At the end of the period, the contribution of JII return variability of 95.91 came from JII's return itself, 3.95 percent of the exchange rate and 0.14 percent of the market risk premium. From the results of the study above shows that the market risk premium does not significantly influence the Jakarta Islamic Index return. This is because the Jakarta Islamic Index shares are based on sharia principles that avoid the element of interest. So that the ups and downs in general economic conditions will not affect the return of the Jakarta Islamic Index listed on the Indonesia Stock Exchange. Then investors will be safer to invest in Jakarta Islamic Index shares compared to conventional shares. 
\section[{Global}]{Global}\par
While the exchange rate of Rupiah against USD has a significant positive effect on the return of the Jakarta Islamic Index. This indicates that the number of foreign investors who invested their capital in Jakarta Islamic Index shares is quite a lot. That is the level of foreign confidence towards Islamic stocks in Indonesia is quite good, so it can be pushed by developments in Indonesian sharia economy.\par
The suggestions for further research should be done by analyzing the effect of market risk premiums and exchange rates on other countries' sharia indexes or international sharia stock indexes with the addition of larger data.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
        \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.346256345177665\textwidth}P{0.04854060913705584\textwidth}P{0.36135786802030456\textwidth}P{0.0366751269035533\textwidth}P{0.008629441624365481\textwidth}P{0.0010786802030456851\textwidth}P{0.0021573604060913703\textwidth}P{0.0010786802030456851\textwidth}P{0.006472081218274112\textwidth}P{0.003236040609137056\textwidth}P{0.008629441624365481\textwidth}P{0.007550761421319797\textwidth}P{0.010786802030456852\textwidth}P{0.0043147208121827405\textwidth}P{0.003236040609137056\textwidth}}
1.\tabcellsep Return of JII\tabcellsep The difference between JIIofthecurrent month minus JII of the prev ious month divided by JII of the previous month\tabcellsep JII\tabcellsep \multicolumn{3}{l}{Return}\tabcellsep \multicolumn{2}{l}{=}\tabcellsep \multicolumn{2}{l}{JII}\tabcellsep t JII ?\tabcellsep 1 ? JII t\tabcellsep t\tabcellsep 1 ?\\
2.\tabcellsep Market Risk Premium\tabcellsep The difference between market return minus risk free interest rate of Ind onesia Bank\tabcellsep \multicolumn{9}{l}{Market Risk Premium = R M - R f}\\
\tabcellsep \tabcellsep The difference between the current month exchange rate minus\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
3.\tabcellsep Exchange rate\tabcellsep the previous month exchange rate divided by the previous month exchange rate\tabcellsep \tabcellsep ER\tabcellsep =\tabcellsep \multicolumn{2}{l}{ER}\tabcellsep \multicolumn{2}{l}{t ER ?}\tabcellsep \multicolumn{2}{l}{1 ? ER t}\tabcellsep t\tabcellsep 1 ?\\
\multicolumn{3}{l}{The type of data used in this study}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{is secondary data from January 2008 to December 2018.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{The  data used in this study were obtained from various}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{sources, including data of Jakarta Islamic Index and}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Composite Stock Price Index from Indonesia Stock}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Exchange and data of interest rate and exchange}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{rate from Bank Indonesia.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.37103174603174605\textwidth}P{0.04047619047619047\textwidth}P{0.03642857142857143\textwidth}P{0.04857142857142857\textwidth}P{0.005396825396825396\textwidth}P{0.2657936507936508\textwidth}P{0.04182539682539682\textwidth}P{0.04047619047619047\textwidth}}
\tabcellsep \tabcellsep \tabcellsep 1\%\tabcellsep \tabcellsep 5\%\tabcellsep 10\%\\
\tabcellsep RJII\tabcellsep -8.794614\tabcellsep \multicolumn{2}{l}{-2.582872}\tabcellsep -1.943304\tabcellsep -1.615087\tabcellsep Stationary\\
\tabcellsep M Rp\tabcellsep -8.473885\tabcellsep \multicolumn{2}{l}{-2.582872}\tabcellsep -1.943304\tabcellsep -1.615087\tabcellsep Stationary\\
\tabcellsep ER\tabcellsep -10,18931\tabcellsep \multicolumn{2}{l}{-2.582872}\tabcellsep -1.943304\tabcellsep -1.615087\tabcellsep Stationary\\
\multicolumn{5}{l}{Stationary test results at the level indicate that all}\tabcellsep \multicolumn{2}{l}{b) Optimum Lag Test Results}\\
\multicolumn{5}{l}{variables are stationary at the level that includes variable}\tabcellsep \multicolumn{2}{l}{Determination of lag in the VAR model is very}\\
of Jakarta Islamic\tabcellsep \multicolumn{2}{l}{Index return (RJII),}\tabcellsep market\tabcellsep risk\tabcellsep \multicolumn{2}{l}{useful to eliminate autocorrelation. The optimum lag test}\\
\multicolumn{5}{l}{premium ( MRP) and exchange rate (ER), because the}\tabcellsep \multicolumn{2}{l}{results are shown as shown in table 3.}\\
\multicolumn{5}{l}{absolute value of the ADF is smaller than the critical}\tabcellsep \\
\multicolumn{3}{l}{value of Mackinnon at the level of 5\%.}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.18398268398268397\textwidth}P{0.053968253968253964\textwidth}P{0.01717171717171717\textwidth}P{0.18398268398268397\textwidth}P{0.01103896103896104\textwidth}P{0.013492063492063491\textwidth}P{0.012265512265512266\textwidth}P{0.0784992784992785\textwidth}P{0.11774891774891774\textwidth}P{0.10057720057720057\textwidth}P{0.07727272727272727\textwidth}}
\tabcellsep \tabcellsep 0\tabcellsep \multicolumn{2}{l}{828.0082}\tabcellsep \tabcellsep NA\tabcellsep 3.00e-10\tabcellsep -13.41477 -13,34618 *\tabcellsep -13.38691\\
\tabcellsep \tabcellsep 1\tabcellsep \multicolumn{5}{l}{844.8537 32,59552 2.64e-10 * 13,54234 *}\tabcellsep -13,26798\tabcellsep -13.43089*\\
\tabcellsep \tabcellsep 2\tabcellsep \tabcellsep 852,338\tabcellsep \multicolumn{2}{l}{14.11619}\tabcellsep 2.70e-10\tabcellsep -13.51769\tabcellsep -13.03756\tabcellsep -13,32266\\
\tabcellsep \tabcellsep 3\tabcellsep \multicolumn{4}{l}{858.1776 10.73008}\tabcellsep 2.85e-10\tabcellsep -13,46630\tabcellsep -12.78040\tabcellsep -13.18769\\
\tabcellsep \tabcellsep 4\tabcellsep \multicolumn{4}{l}{861.1402 5.299003}\tabcellsep 3.15e-10\tabcellsep -13.36813\tabcellsep -12.47647\tabcellsep -13.00594\\
\tabcellsep \tabcellsep 5\tabcellsep \multicolumn{4}{l}{874,4882 23.22331*}\tabcellsep 2.94e-10\tabcellsep -13.43883\tabcellsep -12.34139\tabcellsep -12.99306\\
\tabcellsep \tabcellsep 6\tabcellsep \multicolumn{4}{l}{880.1111 9.508710}\tabcellsep 3.11e-10\tabcellsep -13.38392\tabcellsep -12,08071\tabcellsep -12.85456\\
\tabcellsep \tabcellsep 7\tabcellsep \multicolumn{4}{l}{887.6630 12.40228}\tabcellsep 3.20e-10\tabcellsep -13.36037\tabcellsep -11,85140\tabcellsep -12.74743\\
\tabcellsep \tabcellsep 8\tabcellsep \multicolumn{4}{l}{893.3145 9.005698}\tabcellsep 3.40e-10\tabcellsep -13.30593\tabcellsep -11.59118\tabcellsep -12,60940\\
\tabcellsep \multicolumn{7}{l}{The optimum lag determination used in this}\\
study\tabcellsep is\tabcellsep \multicolumn{2}{l}{based}\tabcellsep on\tabcellsep the\tabcellsep \multicolumn{2}{l}{shortest}\tabcellsep lag\\
\multicolumn{8}{l}{using Akaike Information Criterion (AIC). Based on the}\\
\multicolumn{8}{l}{calculation of the value of Akaike Information Criterion}\\
\multicolumn{5}{l}{(AIC), the optimum lag is at lag 1.}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2020\\
Volume XX Issue V Version I\\
( ) B\\
Research\end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.2727272727272727\textwidth}P{0.1890909090909091\textwidth}P{0.21363636363636362\textwidth}P{0.17454545454545453\textwidth}}
\tabcellsep \multicolumn{2}{l}{Estimation Results}\tabcellsep \\
\tabcellsep RJII\tabcellsep MRP\tabcellsep ER\\
RJII (-1)\tabcellsep 0.263847\tabcellsep 0.303923\tabcellsep 0.016074\\
\tabcellsep (0.31104)\tabcellsep (0.28979)\tabcellsep (0.14780)\\
\tabcellsep {}[0.84827]\tabcellsep {}[1.04876]\tabcellsep {}[0.10875]\\
MRP (-1)\tabcellsep 0.187815\tabcellsep 0.156931\tabcellsep -0.312838\\
\tabcellsep (0.34762)\tabcellsep (0.32387)\tabcellsep (0.16518)\\
\tabcellsep {}[0.54029]\tabcellsep {}[0.48455]\tabcellsep {}[-1.89389]\\
ER (-1)\tabcellsep 0.573712\tabcellsep 0.506682\tabcellsep -0.293432\\
\tabcellsep (0.23481)\tabcellsep (0.21877)\tabcellsep (0.11158)\\
\tabcellsep {}[2.44328]\tabcellsep {}[2.31604]\tabcellsep {}[-2,62980]\\
C\tabcellsep 0,000375\tabcellsep -0.001068\tabcellsep 0.005960\\
\tabcellsep (0.00526)\tabcellsep (0.00490)\tabcellsep (0.00250)\\
\tabcellsep {}[0.07129]\tabcellsep {}[-0.21788]\tabcellsep {}[2.38351]\\
R-squared\tabcellsep 0.105730\tabcellsep 0.128479\tabcellsep 0.176478\\
Adj. R-squared\tabcellsep 0.084437\tabcellsep 0.107729\tabcellsep 0.156871\\
Sum sq. resids\tabcellsep 0.428945\tabcellsep 0.372341\tabcellsep 0.096857\\
SE equation\tabcellsep 0.058347\tabcellsep 0.054361\tabcellsep 0.027726\\
F-statistics\tabcellsep 4,965659\tabcellsep 6.191623\tabcellsep 9,000474\\
Log likelihood\tabcellsep 186,9454\tabcellsep 196,1441\tabcellsep 283.6716\\
Akaike AIC\tabcellsep -2.814545\tabcellsep -2.956063\tabcellsep -4.302640\\
Schwarz SC\tabcellsep -2.726313\tabcellsep -2.867831\tabcellsep -4.214408\\
Mean dependent\tabcellsep 0.004252\tabcellsep 0.002651\tabcellsep 0.004064\\
SD dependent\tabcellsep 0.060978\tabcellsep 0.057549\tabcellsep 0.030195\\
\multicolumn{2}{l}{Determinant residual covariance ( dof adj.)}\tabcellsep 3.52E-10\tabcellsep \\
\multicolumn{2}{l}{Determinant residual covariance}\tabcellsep 3.20E-10\tabcellsep \\
Log likelihood\tabcellsep \tabcellsep 867.6454\tabcellsep \\
\multicolumn{2}{l}{Akaike information criterion}\tabcellsep -13.16378\tabcellsep \\
Schwarz criterion\tabcellsep \tabcellsep -12,89908\tabcellsep \\
\multicolumn{2}{l}{Number of coefficients}\tabcellsep 12\tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 5 :}\end{figure}
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\end{document}
