The Effect of Exchange and Dow Jones Industrial Average (DJIA) on the Jakarta Composite Index (JCI) when the Downtrend Happened Due to the Covid-19 Pandemic

This study analyzes the effect of the Exchange Rate and the Dow Jones Industrial Average on the Jakarta Composite Index during a downtrend due to the COVID-19 pandemic. The impulse response function was used to analyze the permanent effects on an exogenous shock variable on one of them. The study results prove a significant effect of the exchange rate on the Jakarta Composite Index. The same result showed that the most the weakening of the exchange rate of the Rupiah, the Jakarta Composite Index tends to fall. Besides, the Dow Jones Industrial Average harms the Jakarta Composite Index. In the long run, the response given by the Jakarta Composite Index showed a negative level. The USD/IDR exchange rate weakening was very influential on the Jakarta Composite Index. Otherwise, the weakening of the Rupiah exchange rate has led to the decrease of the Jakarta Composite Index. Monetary authorities should reckon on the stock price movements to control the stability of monetary exchange.

showed that both variables do not imply the JCI. Otherwise, the GDP growth and exchange rates had a significant effect on the JCI. In contrast, the study of Bella et al.(2021) using the same components showed that inflation negatively influences the Stock Price Index. Ismawati assessed through multiple regression analysis the change in the Jakarta Composite Index during the COVID-19 pandemic. The results indicated that the Jakarta Composite Index has positively decreased. Besides, inflation and the Dow Jones Index have different impacts on the Jakarta Composite Index.
The research conducted by Robiyanto(2018) showed that the Rupiah/US dollar exchange rate had a significant adverse effect on the Joint Stock Price Index. The result from Damajanti (2018) study proves the significant effect of the rupiah exchange on the CSPI, but the Dow Jones Index has no significant effect on the CSPI.
This research is interested to know the effect of macro variables (the Exchange Rate and the Dow Jones Industrial Average) on the Jakarta Composite Index during a downtrend due to the COVID-19 pandemic. Besides that, it is also to find out indicators of macro variables that have a dominant influence on the Jakarta Composite Index.

2.Variable description 2.1. Jakarta Composite Index (CSPI or JCI)
The Jakarta Composite Index is a composite of the stock prices of listed companies that trade on the Indonesia Stock Exchange. The Jakarta Composite Index is used to measure all stocks' combined performance on the stock exchange (Jogiyanto, 2014). The formula used to calculate the JCI is as follows:

Exchange rate
The exchange rate is the ratio of the value of a country's currency to foreign currencies (Hasibuan, 2005). In the monetary approach, the currency exchange rate is defined as the price at which foreign currency is traded against the domestic currency, and that price is related to the supply and demand for money. The exchange rate is one indicator that affects activity in the stock market and money market because investors tend to be careful when making investments.

Dow Jones Industrial Average (DJIA)
The Dow Jones Industrial Average, commonly called the Dow Jones Index, is a stock market index founded in 1982 by the editor of The Wall Street Journal and the founder of Dow Jones & Company, Charles Dow. Tandelilin(2010) stated that the Dow Jones Industrial Average is the world's most extensive average stock index. Therefore, the Dow Jones Industrial Average movement can affect almost all world stock indexes, including the JCI. The influence of the Dow Jones Industrial Average on the JCI is expected to be positive, in the sense that the increase in the Dow Jones Industrial Average will result in an increase in the JCI on the Indonesia Stock Exchange.

RESEARCH METHOD
The study uses quantitative data types using three research variables: one dependent variable and two independent variables. The dependent variable used is the Jakarta Composite Index (JCI), and the independent variables are the Exchange Rate and the Dow Jones Industrial Average (DJIA).
The study analyses daily data from January 2, 2020 to April 30, 2020. The modeling of time series requires that the latter be stationary. In other words, the series has no trend, no cycle, and no seasonality. To avoid these fallacious estimates, the economists proceed to the stationarities of the chronological series. The first step is to test the stationarity of the data, and if all data are stationary at the level, then the ordinary VAR model can be continued, whereas if all the data are stationary at the first difference level, the model that must be used is the first difference VAR or VECM if there is cointegration.

Result on unit root test
The first step in this research is to test whether the data is stationary or not using the unit root test. In addition, the unit root test is used to determine the VAR model that will be used in the study. The unit root test in this study used the Augmented Dickey-Fuller (ADF) method. If the data used is not stationary at the level, then the data must be changed to the first difference to get stationary data, then the VAR model will be combined with the error correction model to become Cointegrated VAR or commonly known as the Vector Error Correction Model. The results of the unit root test are presented in Table 1.

Optimum Lag Determination
The most crucial thing in this VAR test is finding the optimal lag specification to provide the best predictive power. The choice of lag length is crucial because it can affect the acceptance and rejection of the null hypothesis, lead to estimation bias, and result in inaccurate predictions. The selection of the optimal lag length in the VAR model is mainly to avoid serial correlations between error terms and endogenous variables in the model, which can cause the estimator to be inconsistent.
The determination of lag can use several approaches, namely Likelihood Ratio (LR), Final Prediction Error (FPE), Akaike Information Criterion (AIC), and Schwarz Information Criterion (SC). This study uses the Akaike Information Criterion (AIC) to choose the optimum lag (Table 2). -18.54607 The calculation of the AIC value for each lag shows that the minimum AIC value is obtained during lag 3 for the variables that affect the Jakarta Composite Index (JCI) during a downtrend.

Johansen Cointegration Test
The purpose of the cointegration test in this study is to determine whether the group of variables that are not stationary at these levels meet the requirements of the integration process, namely where all variables are stationary at the same degree, namely degree 1.
Long-term information is obtained by determining the cointegration rank to determine how many equations can explain the entire existing system. Cointegration testing criteria in this study are based on trace statistics. If the value of the trace statistic is greater than the critical value of 5 percent, then the alternative hypothesis stating the number of cointegrations is accepted so that it can be seen how many equations are cointegrated in the system. This test is to determine whether there is a long-term effect for the variables that we will examine. If it is proven that there is cointegration, then the VECM stage can be continued. However, if it is not proven, then VECM cannot be continued. Sign 5%* 3-Cointegration Sign 5%* 3-Cointegration Based on the table above, it can be seen that the trace statistic and maximum eigenvalue at r = 0 are more significant than the critical value with a significance level of 1% and 5%. The null hypothesis states that no cointegration is rejected, and the alternative hypothesis that there is no cointegration cannot be rejected. Thus, the cointegration test results indicate that the JCI, Exchange rate, and DJIA movements have a relationship of stability/balance and similarity of movements in the long term. In other words, all variables tend to adjust to each other in each short-run period to reach their long-run equilibrium.

Impulse Response Function
The Impulse response function serves to see the dynamic response of a dependent variable if it gets a particular shock or an independent variable innovation of one standard deviation. This response indicates the effect of a shock on the dependent variable on the independent variable. Fundamentally, in this analysis, the positive or negative response of a variable to other variables will be known. The response in the long term is usually quite significant and tends to change. In the long term, the response tends to be consistent and continues to shrink. The results of the analysis will be presented in graphical form (Figure 1).

Figure 1: Response to DLN_JCI to innovations using Cholesky (d. f. adjusted) Factors
The Impulse Response Graph of the Jakarta Composite Index shows that the exchange rate shock at the beginning of the period was responded negatively by 6% by the Jakarta Composite Index. However, in the 3rd period to the 4th period, it showed a positive response of 3%. After the 5th period, the response given by the Jakarta Composite Index showed a negative 3-1% range until the end of the period.
The Dow Jones Industrial Average increase responded positively by 4% by the Jakarta Composite Index at the beginning of the period. After the 4th period, the Jakarta Composite Index response to the Dow Jones Industrial Average increase responded negatively in the range of 1-6% until the end of the period.
The USD/IDR exchange rate weakening was very influential on the Jakarta Composite Index. Otherwise, the weakening of the Rupiah exchange rate leads to the decrease of the JCI. The Dow Jones Industrial Average was weakening hurt the Jakarta Composite Index. During the COVID-19 pandemic, the Dow Jones Industrial Average influence was the strongest in influencing the decline in the Jakarta Composite Index during a downtrend and followed by the exchange rate.

Conclusion
During the Covid-19 pandemic, the Dow Jones Industrial Average influence most strongly affected the decline in the Jakarta Composite Index when there was a downtrend and followed by the exchange rate. The weakening of the USD/IDR exchange rate harmed the JCI. A drastic and uncontrollable weakening of the exchange rate would cause difficulties for companies with debt in foreign currency. Companies will have difficulty paying their debts. A decrease in company performance would be a result. The decline in the company's performance will make investors consider selling the company's shares more than buying, causing the JCI to fall. The decline in the Dow Jones Industrial Average will harm the JCI. The weakening of the American stock market, the Dow Jones Industrial Average, will impact the decline in stock market indexes throughout the world, including the Indonesian stock market. Therefore, efforts are needed from the monetary authorities to maintain the stability of the Rupiah exchange rate so that stock price movements are controlled.