THE IMPACTS OF OIL PRICE SHOCKS ON ECONOMIES AND MINING INDEX: NEWEST TIME SERIES EVIDENCE FOR INDONESIA AS EMERGING MARKET

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1 THE IMPACTS OF OIL PRICE SHOCKS ON ECONOMIES AND MINING INDEX: NEWEST TIME SERIES EVIDENCE FOR INDONESIA AS EMERGING MARKET Marla Setiawati*, Sudarso Kaderi Wiryono School of Business and Management, Institut Teknologi Bandung, Jl. Ganesha No , Bandung, Indonesia * Abstract: Indonesia is a net oil importer country since 2004 and oil price has decreased dramatically over the past decade. The research aims to assess the impacts of oil prices shock on economies and mining index. It is startling that little research has been conducted on the nexus between oil price shock and mining index directly especially in Indonesia. In order to fill this gap, the newest evidence comes over the period 2000:01 to 2017:10. This research is using Vector Auto Regressive Model and look in-depth with Impulse Reaction Function and Granger Causality. First, we find change oil price gives significant impact to mining index in Indonesia and the causality is bi-directional both that variables. Second, change oil price gives significant impact to inflation but not to Indonesia s GDP. Shock of oil price will decrease the value of inflation and exchange rate whereas increase the value of mining index and GDP. The reaction of that shock only happens in the short run which is quarter two until quarter four. The practical implication of this research is very useful not only for investor in mining index but also regulator to see the impact of the role Indonesia as a net oil importer. Keywords: oil prices shock; mining index; Vector Auto Regressive Model; Impulse Reaction Function; Granger Causality 1 Introduction The issue of asymmetric effect of oil price shocks and the mitigating impact of change oil price on the economy happened since the end of the 1970s (Kilian, 2004). Hooker (1996) had also wondered the importance of the oil macro-economy relationship. Indonesia is third biggest of emerging countries after China and India in Asia and Indonesia has growth as net oil importer starting from There are two main reasons, why shock of oil price changes need to be considered in Indonesia. First, oil is one important factor in almost each sector, oil is one of the resources that most extensively used factor inputs in the production of goods and services in the 22

2 world, so the changes of oil price can lead on macroeconomic variables and showed in market data like mining index. Mining index is one of important sector which is considered from investor perspective. By knowing either the relation or the causality (more in-depth relationship) between oil prices changes, mining index, and macroeconomic variables, investor can anticipate to invest or not. Second, it is important to assess whether change oil prices do present opportunities for Indonesia s economic development. The aim of this research is to assess the impacts of oil prices shocks on economies and mining index. The macroeconomics variable are Gross Domestic Product (GDP), exchange rate, and inflation. The code of mining index is JKMING, which is important sector that well known along the investor. The novelty and scientific contributions of this paper add value to the literature. First, we do this research with the newest available data start from first quarter in 2000 until fourth quarter in Second, we add the mining index which is one of important sector in Indonesia. Third, we use econometric tools from considering VAR (Vector Auto Regressive) as the best model for these variables and go in-depth with Impulse Reaction Function and Granger Causality. The outline of this research is as follows: Section 2 presents related literature review of this research. In section 3, the dataset and methodology are described. Section 4 discusses the result and discussion. Section 5 concludes this research. 2 Literature Review In develop countries, some researcher focus how changes oil price has impact on macroeconomic variable (Hooker, 1996, Hooker, 1999, Boyd, 2003). Hooker (1996) said that oil price is endogenous variable and related with macroeconomic variables. Hooker (1999) stated that oil price had impact to inflation before 1980 but little after that time. His result was robust to some different specifications like measurement of oil price, breaking lag structure, and sample period. Instead of VAR (Vector Auto Regressive) model, there is CGE (Computable General Equilibrium) model that used by Doroodian and Boyd (2003) to assess whether oil price shock is inflationary in United States. Michieka and Gearhart (2015) examined the short term and long 23

3 term for region in US which Kern (the most oil productive region in US) and found that there is no causality in the short term but there is long term causality between oil price and unemployment. Katircioglu, Sertoglu, Candemir, and Mercan (2015) investigated for OECD countries about oil price and macroeconmic performance. By using panel cointegration test, they concluded that oil price had negatively significant impact on GDP, inflation, and unemployment. In emerging market, Tang, Wu, and Zhang (2010) stated that oil price had negatively affect investment but positively on inflation and interest rate in China. Doğrul and Soytas (2015) assessed the nexus between crude oil, interest rate, and unemployment in Turkey. They found that interest rate and oil price had granger cause unemployment. The previous research also examined sensitivity stock market index to oil price and said there is long term causality from oil price shocks and chemical sectors (Eksi, Senturk and Yildirim, 2012). Cong, Wei, Jiao, and Fan (2008) assessed the relationship between oil price shocks and Chinese Stock Market. By using multivariate VAR, they found that oil price shocks do not affect most stock returns, but might magnify the speculations in mining index and petrochemicals index, which magnify their stock returns. Paye, Zehnwang, Wesseh, and Tutdel (2017) presented the effect of oil price shocks in Liberia as emerging country and found that the impact of oil shocks is limited in the short term. Timilsina (2015) assessed oil price on global economies and said that the impacts are strong for oil importer like India and China. Fang and You (2014) had done the research about the effect of oil shocks to stock market returns for China, India, and Rusia. Their research found that their result very contradicted with developed market. Changing of oil price had also impact of some countries in ASEAN like Malaysia and Thailand (Hersugondo, Robiyanto, Wahyudi and Muharam, 2015). In Taiwan, Wu and Ni (2010) examined the nexus between oil price, inflation, and interest rate. They found that oil price gives affect the inflation and the otherwise. Cunado, Jo, and Gracia (2015) assessed for four countries in Asia, namely Japan, Korean, Indonesia, and India about the impact of oil price shocks on economy activity. They found there is positive responses of GDP to oil shocks. Specifically, In Indonesia, Agusman and Elis (2008) already examined the impact of oil price of nine sectors (overall sector index in Indonesia Stock Market). They found that oil prices changes do not have significant impact on industry stock return. They used data from January 2006 until June Aprina (2014) assessed the impact of crude oil price on rupiah s rate and she found 24

4 that increasing crude palm oil price lead to an appreciation of rupiah as Indonesia s currency. She used the data from Rahmanto, Hira Riga, and Indiana (2016) described the effect of crude oil price changes on the Indonesia Stock Market. They used the data from 2007 until 2015 and found that changes in crude oil price have significant impact for sector of agriculture and consumer goods. Reza and Sumantyo (2017) assessed the performance of Indonesia s mining sector price index and concluded that world oil prices affect positively but not significant on stock price index of the mining sector. They used the data from In this research, we focus how change oil price gives impact on economies and index mining in Indonesia as emerging market. Starting 2004, Indonesia is a net oil importer country. The main reason is oil production in Indonesia were decreasing significantly. Moreover, impact of oil prices shocks on economies might be different for each country depends on whether the country is net oil importer or net oil exporter and whether it happens in emerging market like Indonesia or developed market. In this research, we use the quarterly data from first quarter 2000 until fourth quarter 2017 which is the newest data as evidence in Indonesia. We also focus on econometric tools which is Vector Autoregressive model and go in-depth with Impulse Reaction Function and Granger Causality. We also add the mining index to have better understanding when investing in mining sector. 3 Data and Methodology 3.1 Data We use the five main variables (see table 1), namely oil prices changes, Indonesia s GDP, mining index (JKMING), exchange rate and inflation. Oil price is using data from Brent world index. In this research, we use the delta oil price like equation 1. GDP data is from Federal Reserve Economic Data database (FRED) at the St. Louis Federal Reserve Bank. Mining index and exchange rate are from investing.com. Inflation rate is from Bank of Indonesia website. All the data is quarterly from first quarter 2000 until fourth quarter

5 oil lnoilt lnoilt 1 (1 Table 1. Data. Variable name Description Sources GDP Gross domestic product In billion dollar FRED Inflation Percentage each quarter Bank of Indonesia Brent oil price Dollar per barrel which is represent the world oil market Brent world index Exchange rate Rupiah to USD Investing.com Mining index Stock price in mining sector Investing.com 3.2 Methodology In this paper, an unrestricted vector auto-regression (VAR) model is employed to investigate the complexities of the dynamic connections between shock of oil prices and macro economy variables and mining index. We use VAR model, which is the model that stated all variables are regarded as endogenous variables. It means that dependent variable could affect the independent variable and each independent variable could affect each other. Because we use VAR model, all the data must be stationary. We conducted the stationary analysis by using Augmented Dickey Fuller Test. (Dickey and Fuller, 1979). Figure 1 below is the flow chart how to do with VAR model. Based on impulse response functions, we analyze the interactive responses between shock of oil prices and macro economy variables and mining index. We also analyze using Granger Causality which is the method to infer not only correlation between variables but also the causality (stronger relationship between variables). We obtain the contribution of oil price shocks to the GDP, exchange rate, mining index, and inflation. 26

6 Data VAR model in level data Don t have a trend Stationary Test Have a trend Make it in difference data VAR model in difference data Not exist Cointegation Test Exist VECM Residual Diagnostic Analysis of IRF & Granger Causality Fig. 1. Flow chart VAR model From figure 1 above, we start stationary test of the data. If the data has no trend, we directly do analyze using VAR whereas make it in difference data for the data that has a trend. We do analyze using residual diagnostic, namely serial correlation and heteroskedasticity. After passing those diagnostic, we can analyse using Impulse Reaction Function (IRF) and Granger Causality. 4 Result and Discussion 4.1 Stationary Analysis By looking at the figure 2 and 3, we know which variables that have trend or not. Figure 2 shows us the graph of GDP and inflation rate in Indonesia. Figure 3 shows us the graph of exchange rate, mining index, and change oil price. As visual, we can conclude that GDP, exchange rate, and mining index have a trend but we need analyze in statistical way (look table 2). 27

7 Fig. 2. (a) GDP Indonesia; (b) Inflation rate Indonesia. Fig. 3. (a) Exchange rate; (b) Mining index; (c) change oil price Table 2. Unit Root Test. Null Hypothesis: The data has unit root test. Variable name Data Probability Trend? GDP Level 1,0000 Yes GDP 1 st difference No Inflation Level Yes Inflation 1 st difference No Exchange rate Level Yes Exchange rate 1 st difference 0,0000 No Mining index Level Yes Mining index 1 st difference No Change oil price Level No 28

8 From the table 2 above, we know that almost all variables have a trend except change in oil price. By using the 1 st difference data for GDP, inflation, exchange rate, mining index and level data for change oil price, we use the data that has no trend. After all the data has no trend, we execute the data using VAR model. 4.2 Vector Auto Regressive Model Table 3 below shows us the result of VAR model. There are five dependent variables, namely GDP, inflation, exchange rate, JKMING, and change oil price. Table 3. VAR Model. Dependent variable GDP Inflation (INF) Equation GDP = (EXCH t-1) (GDP t-1) (inf t-1) (JKMING t-1) (DOIL t-1) INF = e-06(EXCH t-1) e-08(gdp t-1) (INF t-1) e-05 (JKMING t-1) (DOIL t-1) Exchange rate (EXCH) EXCH = 0.227(EXCH t-1) (GDP t-1) (INF t-1) (JKMING t-1) (DOIL t-1) Mining Index (JKMING) JKMING = (EXCH t-1) (GDP t-1) (INF t-1) (JKMING t-1) (DOIL t-1) Change oil price (DOIL) DOIL = (EXCH t-1) e-07(GDP t-1) (INF t-1) (JKMING t- 1) (DOIL t-1) From the Table 3, we know that exchange rate and inflation decrease value of GDP. The changes of oil price do decrease the value of inflation. The inflation rate, change oil price, and JKMING do decrease the value of exchange rate. The exchange rate, GDP, and inflation also do decrease the value of change oil price. 29

9 4.3 Residual Diagnostic After having the VAR model, to do analysis like impulse reaction function and granger causality, we need to check the residual diagnostic. There are two main types of residual diagnostic, namely serial correlation and heteroscedasticity. The model is good enough, if it has passed serial correlation and heteroscedasticity. The model must have no serial correlation and no heteroscedasticity. Serial correlation LM Test is an alternative to the Q-statistic for testing serial correlation. The test belongs to the class asymptotic test known as Langrange multiplier (LM). Heteroscedasticity means changes of variance. The model is good, if it is homoscedasticity. Table 4 and Table 5 are the result of serial correlation LM Test and heteroscedasticity. By looking at probability and null hypothesis, we can know whether the model has serial correlation or no and has heteroscedasticity or no. Table 4. Serial Correlation. Null Hypothesis: There is no serial correlation Variable name Prob. F-stat Serial Correlation GDP No Inflation No Exchange rate No Mining Index No Change oil price No Table 5. Heteroscedasticity Null Hypothesis: The data is no heteroscedasticity Variable name Prob. F-stat Heteroscedasticity GDP No Inflation No Exchange rate No Mining Index No Change oil price No The result from table 4 and 5 shows us that all variables are no serial correlation and no heteroscedasticity. 30

10 4.4 Impulse Reaction Function Impulse response is a graph that showed how if one standard deviation of shock (innovation) of a variable will affect another variable and how it is developed over time. Because we use quarter data, we can say short run effect if the lag is not more than 4 lag. Fig. 4. exchange rate; GDP. (a) Response of (b) Response of Fig. 5. (a) Response of inflation; (b) Response of JKMING The result of impulse reaction function shows that the impact of change oil price to exchange rate and inflation is negatively significant. Otherwise, the impact of change oil price to GDP and mining index is positively significant. The significant happens only in the short run which is until lag 2 to lag 4. As net oil importer country, we buy the oil using dollar. If oil price increase, we will buy it using dollar and it will make rupiah become weaker than before. That is one reason why increasing of change oil price to exchange rate like figure 4a. Indonesia is a net oil importer, so when the change of oil price increase, it will give positive impact to our GDP. 4.5 Granger Causality The result of granger causality is showed in table 6. We observe that change of oil price does granger cause mining index and mining index does granger cause change oil price. Causality between change oil price and mining index is bi-directional in Indonesia. For investor, it can be a good or bad signal depends on what happen on change oil price. Change oil price also does 31

11 granger cause inflation, the reason is oil price is one of the most extensively used factor inputs in the production of goods and services. If the oil price increase, it will make the price of goods or service increase. Inflation does granger cause GDP. Change oil price and exchange rate also have the bi-directional causality. Mining index does granger cause exchange rate but it does not happen the other way around. Table 6. Granger Causality Test Result Null Hypothesis Probability Reject Null Hypothesis GDP does not Granger Cause Exchange rate No Exchange rate does not cause GDP No Inflation does not granger cause exchange rate No Exchange rate does not granger cause inflation No Mining Index does not granger cause exchange rate Yes*** Exchange rate does not granger cause mining index No Change oil price does not granger cause exchange rate Yes** Exchange rate does not granger cause change oil price Yes*** Inflation does not granger cause GDP Yes** GDP does not granger cause inflation No Mining index does not granger cause GDP No GDP does not granger cause mining index No Change oil price does not granger cause GDP No GDP does not granger cause change oil price No Mining index does not grange cause inflation No Inflation does not granger cause mining index No Change oil price does not granger cause inflation Yes* Inflation does not granger cause change oil price No Change oil price does not granger cause mining index Yes** Mining index does not granger cause change oil price Yes*** *** indicates 1% significance level ** indicates 5% significance level 32

12 * indicates 10% significance level 5 Conclusions The aim of this research is to examine the impacts of oil prices shocks on economies and mining index. The macroeconomics variable are Gross Domestic Product (GDP), exchange rate, and inflation. We examine the variables by using the newest data for Indonesia which is from first quarter 2000 until fourth quarter Our result conclude that change oil price gives significant impact to mining index in Indonesia and the causality is bi-directional both that variables. Bi-directional causality also happens between change oil price and exchange rate. Change in oil price give significant impact to inflation but not to Indonesia s GDP. Shock of oil price will decrease the value of inflation and exchange rate and increase the value of mining index and GDP. The reaction of that shock only happens in the short run which is quarter two until quarter four. The impact for Indonesia as net oil importer is exchange rate will depressed in the short run. Our result is similar with Paye, et.al 2017 that stated the impact of oil price in emerging market is limited in the short run. But our result is different from Reza and Sumantyo (2017) because our result shows that the change oil price gives impact significantly to mining index in Indonesia. The practical implication of this research is very useful not only for investor in mining index but also regulator to see the impact of the role Indonesia as a net oil importer on main economies variable which is Gross Domestic Product and also design policy responses to reduce some impacts. The implication is investor in mining sector will decrease as decreasing of oil price. The further research can be conducted to all sector and go in depth with much more sophisticated tools in econometrics related with political connection and oil price. 33

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