Revisiting the Effects of Oil price on Exchange Rate: Asymmetric Evidence from the ASEAN-5 Countries

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1 Revisiting the Effects of Oil price on Exchange Rate: Asymmetric Evidence from the ASEAN-5 Countries Khalid M. Kisswani, PhD Department of Economics and Finance Gulf University for Science and Technology Hawally Kuwait In this paper we use quarterly data from 1970:Q1 to 2016:Q4 to explore the asymmetric relationship between real oil prices and real exchange rates for selected ASEAN countries. This is done by employing the nonlinear Autoregressive Distributed Lags (NARDL) approach of cointegration, in the presence of structural breaks. The empirical results show long-run asymmetry for Indonesia and Malaysia only, when considering structural breaks. The paper, furthermore, examined the causality direction for the oil price-exchange rate nexus. The results show mixed results, as we find bidirectional causality between oil price (increase and decrease) and exchange rate in some cases, meanwhile, we find unidirectional causality running from either oil price increase or decrease to exchange rate in other cases. Keywords: ASEAN; real oil prices; exchange rates; NARDL; Asymmetry 1. Introduction The seminal work of Hamilton (1983) strived a diverse body of literature on understanding the impacts of oil price shocks on macroeconomic variables, given that he documented the connection between the oil price changes and the US business cycles. The literature, since then, was extended to the oil price-exchange rate nexus (Krugman, 1983; Golub, 1983; Rogoff, 1991, among others),especially that crude oil markets are mostly invoiced by the US dollar. Furthermore, extensive work revealed the importance of oil price fluctuations in explaining the exchange rate movements, besides, showing the different channels through which the oil prices notably influence the exchange rate (see for example, inter alia: Bloomberg and Harris (1995), Amano and Norden (1998), Chaudhuri and Daniel (1998), Sadorsky (2000), Chen and Chen (2007), Benassy- Quere et al. (2007), Huang and Guo (2007), Zhang et al. (2008), Narayan et al. (2008), Lizardo and Mollick (2010), Zhang and Wei (2010), Benhmad (2012), Reboredo (2012), Tiwari et al. (2013), and Kisswani (2016a, 2016b)). In general, Benassy-Quere et al. (2007) summarize the main two avenues that theoretically explain the link between oil prices and exchange rates. The first avenue considers oil as a major determinant of the terms of trade, where it describes a simple model with two sectors for tradable and non-tradable goods, and each sector uses a tradable input (oil) and a non-tradable one (labor). If oil price increases, then, for the tradable sector to remain competitive, the labor price decreases. Assuming the non-tradable sector is more energy intensive than the tradable one, then, its output price rises and so does the real exchange-rate. The second avenue focuses on the balance of payments, where it considers the increases in oil prices will lead to wealth transfer from oil importing to oil exporting countries. This avenue describes how the effect of oil price on exchange rate will mainly depend on the 8

2 geographic distribution of oil imports across oilimporting countries. The oil market over the last three decades has noticed exceptional price fluctuations. Thus, it stays valuable to continue examining the effect of oil price variations on the exchange rate, especially that the empirical findings about the oil price-exchange rate nexus show mixed results (For recent evidence, see, inter alia: Chinn (2000), Camarero and Tamarit (2002), Akram (2004), Chen and Chen (2007), Huang and Guo (2007), Narayan et al. (2008), Chen et al. (2010), Wu et al. (2012), Benhad (2012), Mohammadi and Jahan-Parva (2012), Reboredo (2012), Tiwari et al. (2013), Czudaj and Beckmann (2013), and Kisswani (2016a, 2016b)). Unlike the vast majority of literature available investigating oil price-exchange rate dynamics for developed and industrialized countries, this paper focuses on five primary economies of South East Asia, namely Indonesia, Malaysia, the Philippines, Singapore and Thailand (hereafter denoted as ASEAN-5). There are several reasons for selecting the ASEAN-5 countries to be the case study in this paper. For example, the monetary authorities of the ASEAN countries carried out a fundamental reform in the exchange rate regimes since the 1997 Asian financial crisis. The tremendous changes and fluctuations in exchange rates since then have raised a higher risk of international economic transaction (Liang et al., 2013). Moreover, less attention has been paid to the ASEAN-5, although the economic growth in these countries is heavily tied to energy growth, indicating that these economies are more exposed to oil shocks (Kisswani, 2016b). With this point in mind, this paper aims to contribute to this research gap, by examining the oil price-exchange rate nexus for the ASEAN-5 during the period of 1970:Q1 to 2016:Q4. This is done by employing the nonlinear autoregressive distributed lags model (NARDL) of Shin et al. (2013). The NARDL model allows to obtain consistent long-run inferences through bounds tests regardless of the integration orders of the variables in the model. A number of recent studies have found this model constructive for modeling nonlinear relations between economic and financial time series.1 This paper takes the literature forward in a different way by contributing in four major folds. First, it applies the recently developed state-of-art econometric methodology (NARDL) that tests for asymmetric effect through decomposing the oil price change into positive and negative shocks as compared to the vast majority of studies in this field that did not pay attention to the nonlinearity in the oil price-exchange rate nexus. Hence, applying the NARDL model is considered the major contribution, as this paper tests the asymmetric effect (through the NARDL model) of oil prices on exchange rates. Second, in this paper, we run the analysis by using the oil price invoiced in national currency rather than the US dollar. This would mean that variations in oil prices conveyed in national currency could be attributed to changes in the exchange rate or the national price level, but not the oil price itself. Third, the paper uses long and recent data for the ASEAN-5, as compared to the others. Finally, it employs the Toda and Yamamoto (1995) non-causality test, which fits better than other conventional tests when examining the direction of causality assuming nonlinear association between oil prices and exchange rates. Briefly presaging the main results, the paper finds asymmetric effect in the long-run for 9

3 Indonesia and Malaysia only, however, a symmetric effect is confirmed for the rest three countries, after incorporating the structural breaks. As for the shortrun analysis, the findings confirm asymmetric effect in case of Malaysia only. The rest of the paper is organized as follows. Next Section describes the model employed and illustrates the data used. Section 3 reports the empirical findings, whereas, Section 4 concludes. 2. Econometric methodology The sample data in this paper is extracted from the International Financial Statistics (IMF) and it is in 1 For other applications of the NARDL model, see Apergis and Payne (2014), Choudhry et al. (2014), Van Hoang et al. (2016), Bahmani-Oskooee and Fariditavana (2015), Kisswani and Elian (2017) among others. quarterly frequencies from 1970:Q1 to 2016:Q4 for the ASEAN-5 countries. The sample contains nominal Brent oil price (quoted in US dollars), consumer price index (base = 2010), and nominal exchange rate (defined as national currency per US dollar). The definition of the exchange rate, national currency per US dollar, indicates that the US dollar appreciates (depreciates) when the exchange rate rises (falls). Real exchange rates are constructed using consumer price indices by converting nominal exchange rates relative to the US dollar. To obtain the real oil price, the US dollar price is converted to national currency price, using the exchange rate, and then it is deflated by the national consumer price index. All variables are measured in logarithms. The methodology in this paper follows the analytical framework of Chaudhuri and Daniel (1998) and Chen and Chen (2007) in hypothesizing the association between real exchange rates and real oil prices (in national currency) as follows: Et = β0 + β1pt + εt (1) where Et is real exchange rate, Pt is real oil price, and εt is the error term. As mentioned before, all variables are measured in logarithms. Interestingly, when examining the current literature, one noticeably can detect a lack of papers that evaluate the focal link within nonlinear structure. The vast majority of the literature that examined the oil price-exchange rate nexus was done in a linear structure presuming symmetric association, even though the majority of the macroeconomic variables follows nonlinear characteristics. Jammazia et al. (2015) summarize the possible reasons that could have generated the nonlinearity in the relation between oil prices and exchange rate such as: successive episodes of economic and financial crises, black swan events, geopolitical tensions, structural changes in business cycle, and heterogeneous economic agents. As such, we think applying linear models might not be the proper methodology in examining the relationship between oil prices and exchange rate, as it could offer deceptive proposition on such association, and this might have been the reason for the mixed results found in the literature about this nexus. Moreover, the response of real exchange rate to positive change in oil prices (increase) could be different than the response to the negative change (decrease). Consequently, in this paper we examine the possible asymmetric association between oil prices and exchange rate for the ASEAN-5 countries by applying a nonlinear analysis as compared to the majority of research done in this field that followed a linear approach. Fariditavana (2015), Kisswani and 10

4 Elian (2017) among others. To this end, the nonlinearity (asymmetry) will be examined by employing the recently developed nonlinear autoregressive distributed lags (NARDL) model of Shin et al. (2013). This technique develops the autoregressive distributed lags (ARDL) bounds testing approach of Pesaran et al. (2001) to allow for estimating asymmetric long-run as well as short-run coefficients in a cointegration framework. The NARDL model overpowers other conventional cointegration models as it is not affected by some of the problems the conventional models might face. For example, the NARDL model can be utilized regardless of the stationarity of the variables used, as long as none of the variables is I(2). Furthermore, the NARDL model performs better when investigating the cointegration relations in small samples (Romilly et al., 2001). By using the NARDL model, we hypothesize the oil price-exchange rate long-run nexus by the following equation: In Eq. (2), α = (α0,α1,α2) is a cointegration vector or a vector of long-run parameters to be estimated, and the disturbance (et) is the standard error term that follows iid process with zero mean and finite variance, and it is independently distributed. In Eq. (2), Pt+ and Pt are the partial sums of the positive and negative changes in Pt: partial sum decompositions, allowing for identifying asymmetric outcomes in both the long and the shortrun. As a matter of fact, the description of the NARDL allows for joint examination of nonstationarity and nonlinearity in the setting of an unrestricted error correction model. From Eq. (2), α1 depicts the long-run relation between exchange rate and oil price increases, meanwhile, α2 portrays the long-run relation between exchange rate and oil price decreases. We further expect that the increases in oil price will cause different long-run variations in the exchange rate as compared to oil price decreases of the same magnitude, i.e. α1 α2. Hence, the longrun nexus as described by Eq. (2) signifies asymmetric long-run oil price pass through to exchange rate. Eq. (2) can be integrated in an ARDL setting as follows: where Δ is the difference operator, m and q are the lags length, and ut is the serially uncorrelated error term. According to Pesaran and Shin (1999), the ARDL model does not require symmetry of lag lengths, where each variable can have a different number of lag terms, unlike other types of cointegration tests. The long-run coefficients Shin et al. (2013) show that the NARDL setting is a cointegration test that utilizes positive and negative will represent the long-run effects of oil price increases and decreases, respectively, on the exchange rate. 11

5 captures the short-run effect of oil price increases on exchange rate, while shows the short-run effect of oil price decreases. The design of Eq. (5) shows that the model describes the asymmetric long-run effect of oil price variations on exchange rate as well as the asymmetric short-run impact. Although the NARDL methodology can be employed despite the stationarity of the variables used whether it is I(1) or/and I(0), however, the presence of I(2) variable invalidates the cointegration test. For this reason. This paper tests the order of integration of the variables to verify that none is I(2). For this reason we apply the Phillips and Perron (1988) unit root test. Phillips and Perron proposed nonparametric transformations of the test statistics from the original Dickey-Fuller test, controlling for serial correlation when testing for a unit root. The Phillips and Perron assume: where at ~ serially correlated. Hence, the Phillips and Perron test takes the form of: The unit root test generally examines the null hypothesis: variable has unit root (H0: δ=0), against the alternative: variable is stationary (H1: δ <0). The Phillips and Perron test does not require specifying the form of the serial correlation of ΔYt under the null. In addition, the Phillips and Perron test does not require that the a s are conditionally homoscedastic Once variables are confirmed none is I(2), then, we proceed by estimating Eq. (5) using the standard OLS method including the significant number of lags to describe the most consistent demonstration of the NARDL model. Pesaran et al. (2001) show that testing for cointegration is conducted by utilizing the F-test to establish joint significance of the lagged level variables. The null hypothesis of no cointegration (H0: β0=β1 = β2 = 0) is tested against the alternative of cointegration (H1: β0 β1 β2 0). Pesaran et al. (2001) tabulated two sets of critical values (upper bound and lower bound). Rejecting the null hypothesis is simply confirmed when the computed F-statistic of the cointegration test exceeds the upper bound critical value; implying a long-run cointegration relationship among the variables of the model. Alternatively, if the computed F-statistic falls below the lower bound critical value, then the null hypothesis of no cointegration can t be rejected, meaning that we fail to find a long-run cointegration relationship among the variables of the model. Finally, if the computed F-statistic value falls within the bounds, then, the test is inconclusive. In testing the long-run and short-run asymmetric effects, once cointegration is confirmed, we follow the description of Bahmani-Oskooee and Aftab (2017). According to Bahmani-Oskooee and Aftab (2017), the long-run asymmetry effects of oil price volatility on exchange rate is examined by applying the Wald test (distributed as χ2 with one degree of freedom) for the long-run normalized coefficient estimates attached to Pt+ and Pt variables in Eq. (5). The asymmetry requires that the coefficients are significantly different, that is Likewise, the short-run asymmetry effects is examined by testing if the sum of the short-run coefficients estimates of ΔPt i+ and ΔPt i are statistically different, i.e., if 12

6 in Eq. (5).2 This is also done by employing the Wald test. 2 Although Bahmani-Oskooee and Aftab (2017) describe other methods to test short-run asymmetry such as if the number of lags on the ΔPt+ and ΔPt are different, or, if the size or sign of the estimated What is more in this paper is that we pay attention to the possibility of the presence of structural breaks in the sample data, because ignoring such breaks and not incorporating it in the model could give misleading results, that is failing in finding evidence of long-run relationship (see: Gregory et al., 1996). The structural breaks, when ignored in the analysis, could cause unstable cointegrating relation, and this might be a reason for not finding long-run relationship among the variables. For this reason, we investigate if our sample data encounters structural breaks. Our sample data ( ) went through many remarkable episodes that could have caused shifts in the involved variables. Examples of such episodes are: the oil embargo in 1973, the regime change in Iran in 1978, Iraq-Iran war in the 1980s, the Plaza Accord in 1985, the Asian financial crisis in 1997, the invasion of Kuwait in 1990, the September 11 attacks in 2001, the 2008 US mortgage crisis, and changes in exchange rate regimes from fixed to managed or free float. Accordingly, we utilize Bai and Perron (1998) test for structural breaks. The test shows global optimization procedures for identifying m-multiple breaks which minimize the sums-ofsquared residuals of the regression model. In this test, we allow for a maximum number of 5 breaks, employ a trimming percentage of 15%, and use the 5% significance level for the sequential testing.3 If the data shows evidence of structural breaks, then we apply the NARD test one time without considering the structural breaks and another time by including these breaks. When incorporating the structural breaks, we include a dummy variable for each break date. The dummy variable assumes a value of 0 before the break date and a value of 1 for the break date and afterward. Pesaran et al. (2001) show that incorporating dummy variables (one-zero) does not affect the inferences drawn about the cointegration among variables. Finally, we conclude our investigation of the oil price-exchange rate nexus by examining the direction of causality between the variables involved. The causality test is applied by utilizing the Toda and Yamamoto (1995) model. This test ignores any potential non-stationary or cointegration between the series when causality is examined, as compared to other conventional causality tests. The general Toda and Yamamoto causality test between any two variables (Yt and Xt) can be applied by estimating the following VAR model: coefficients of ΔPt i+ and ΔPt i is different at each individual lag, the Wald test remains the most appropriate type of asymmetry test. 3 For more details, see Bai and Perron (1998) where the null hypothesis of non-causality from Xt to Yt in (8) can be stated as: H0:θ1i=0, for i, and the null of non-causality from Yt to Xt in (9) is: H0:η1i=0, for i. 3. Empirical findings 3.1. Unit root test 13

7 We begin the analysis by examining the order of integration of the data variables (stationarity status). It is a common practice in empirical analysis to examine the stochastic properties of the series considered in the model by investigating their order of integration. To do so, we employ the Phillips- Perron (1988) unit root test. Table (1) shows the results of the test. The findings indicate that none of the series is integrated of order 2. As a matter of fact, the results from Table (1) show that all variables are integrated of order 1 (i.e. I(1)) at the 1% significance level. This means simply that we can proceed to estimate the NARDL model. INSERT TABLE 1 HERE 3.2. Structural breaks test Now we examine if the series suffer from structural breaks, as this could cause significant changes in the variables which might affect the cointegration vector. The presence of structural breaks and not considering it might be a reason for not finding cointegration association among the variables. For this, before we employ the cointegration tests (NARDL), we examine coefficients of ΔPt i+ and ΔPt i is different at each individual lag, the Wald test remains the most appropriate type of asymmetry test.3 For more details, see Bai and Perron (1998) the data set for structural breaks by using the Bai and Perron (1998) structural break test. The results of the test are reported in Table (2). The findings from the table clearly show that all countries suffer from 4 breaks. For Indonesia, breaks are around: 1978: Q4, 1986: Q3, 1997: Q4 and 2005: Q4. For Malaysia, breaks are around: 1979: Q3, 1988: Q2, 1997: Q3 and 2006: Q4. For Philippines, breaks are around: 1983: Q2, 1990: Q3, 1997: Q4 and 2007: Q2. For Singapore, breaks are around: 1979: Q2, 1990: Q3, 1998: Q2 and 2010: Q1. Finally, for Thailand, breaks are around: 1977: Q4, 1984: Q4, 1997: Q3 and 2006: Q4. The obtained structural breaks will be incorporated in the NARDL model (1 or 0), as explained previously. INSERT TABLE 2 HERE 3.3. Nonlinear ARDL bounds test without structural breaks To examine if structural breaks are significant in the cointegration test, we start the empirical analysis by running the NARDL model without incorporating the structural breaks, and examining the asymmetric effect of oil prices. The results reported in Table (3) show that cointegration is found only in the case of Indonesia, Malaysia and Singapore. This indicates long-run relationship between oil price and exchange rate only for the mentioned countries at the 1% and 5% significance level, as the F-statistics for the three countries exceed the upper bound critical values of Pesaran et al. (2001). However, for the Philippines and Thailand we fail to find evidence of long-run relation among the variables. This might be due to the fact that we did not consider the structural breaks at this point. INSERT TABLE 3 HERE To proceed with the asymmetry test, we run the unrestricted error correction model of Eq. (5), for Indonesia, Malaysia and Singapore, to examine the long-run and short-run asymmetric effects of oil prices. The findings are reported in Tables (4) and (5), where Table (4) reports the long-run effects and Table (5) reports the short-run effects. Moreover, Table (4) reports the error correction coefficient (ECMt-1), which is needed to be significant and less than one in absolute value. ECMt-1 measures the 14

8 adjustment speed toward the long-run equilibrium. In Table (4), we report, as well, several diagnostic tests that characterizes the consistency of the model such as the Lagrange Multiplier (LM) statistic (as a measure of testing the serial correlation of the residuals, and has a χ2distribution with two degrees of freedom), the CUSUM and CUSUMSQ tests of Brown et al. (1974) (as measures of the stability of the model, where the results are reported as S for stable and NS for not stable), and the adjusted R2. The Wald test results of the long-run asymmetric relationship are reported in Table (4), meanwhile, Table (5) reports the Wald test for the short-run dynamics. INSERT TABLE 4 HERE INSERT TABLE 5 HERE The long-run Wald test (from Table (4)) shows that the effect of oil prices takes the asymmetric structure in case of Malaysia and Singapore only, but the effect is symmetric in case of Indonesia. The estimates of the positive and negative shocks show that an increase in oil prices leads to an appreciation in the US dollar (depreciation in national currency), but the decrease in oil prices leads to a depreciation in the US dollar (appreciation in national currency). Moreover, the short-run Wald test (from Table (5)) confirms the asymmetry relationship for Malaysia and Singapore, however, the short-run dynamics follow symmetric effect for Indonesia. The diagnostic tests show that the model for the three countries are free of autocorrelation. Besides, the cointegration models are stable according to the CUSUM test, but not stable for Indonesia and Malaysia according to the CUSUMSQ test, but stable for Singapore Nonlinear ARDL bounds test with structural breaks In this part we examine the long-run relationship between oil prices and exchange rate by including the structural breaks, and we test the asymmetry effect accordingly. The cointegration results are reported in Table (6). The findings show clearly that incorporating the structural breaks have improved the results. Table (6) shows evidence of long-run relationship for all countries as compared to the longrun findings without incorporating the structural breaks. So, Table (4) confirms cointegration for all countries at the 5% significance level or less. INSERT TABLE 6 HERE Now we turn our attention to the asymmetric effect in the long and short-run. The long-run effects are reported in Table (7), and the short-run effects are reported in Table (8). Besides, the diagnostic tests are reported as before. INSERT TABLE 7 HERE INSERT TABLE 8 HERE The error correction coefficients (ECMt-1) from Table (7) confirm significant long-run with faster adjustments toward the equilibrium as compared to the same coefficients without incorporating the structural breaks. By checking the Wald test for all the countries from Table (7), we find evidence of long-run asymmetry for Indonesia and Malaysia only. Incorporating the structural breaks show no evidence of asymmetric effect in the long-run for Singapore any more, as compared to the results found previously when ignored the breaks. We can conclude that the effect of oil price increase is different from oil price decrease in the long-run only for Indonesia and Malaysia, but the effect is the symmetric for the rest countries. When checking the short-run effects in Table (8), we find evidence of 15

9 asymmetry in case of Malaysia only, but symmetric effect for all other countries. This is evident by the Wald test in Table (8) Causality tests The final step in our analysis is examining the causality direction between the variables, using Toda and Yamamoto non-causality test. Table (9) reports these results. It is evident from Table (9) that there is bidirectional causality between oil price (increase and decrease) and exchange rate, in case of Indonesia. Furthermore, the same bidirectional relation is confirmed between oil price increase and exchange rate in case of Thailand, but, in case of Malaysia, it is between oil price decrease and exchange rate. However, a unidirectional relationship is confirmed running from oil price (increase and decrease) to exchange rate in case of Singapore, but a unidirectional relationship is confirmed running from oil price decrease to exchange rate in case of Philippines and Thailand. 4. Concluding remarks This paper intends to examine the asymmetric effect of oil price (in national currency) on exchange rates (national currency/us dollar) of ASEAN-5 members from 1970:Q1 to 2016:Q4 using the nonlinear ARDL methodology. The main contribution of the paper comes from focusing on the asymmetric effect as compare to the major work done that assume linear effect. The paper, furthermore, incorporates the structural breaks of the data in the analysis as it is shown in the literature that ignoring the breaks could mislead the findings. The empirical findings confirm long-run asymmetric effect in case of Indonesia and Malaysia only, meanwhile, we did not find any evidence of such relation in case of the rest countries, where the empirical findings show symmetric effect in the long-run for Philippine, Singapore, and Thailand. As for the short-run dynamics, we find asymmetric effect for Malaysia only, where the empirical findings show symmetric short-run effect for Indonesia, Philippines, Singapore and Thailand. The final step was testing he causality direction between the variables, using the Toda and Yamamoto non-causality test. The findings show mixed results where we find bidirectional causality between oil price increase and decrease on one side, and exchange rate on the other side, and unidirectional running from either oil price increase or decrease to exchange rate. As a final word, we believe an extension of the paper can be by adding the exports and imports to the model, as these variable could have influence on exchange rate, and this is our future plan. References Akram Q. F. (2004). Oil prices and exchange rates: Norwegian experience. Econometrics Journal, 7: Amano, R., Norden, S.V. (1998). Oil prices and the rise and fall of the US real exchange rate. Energy Policy. 17: Apergis, N., Payne, J. (2014). Resurrecting the size effect: Evidence from a panel nonlinear cointegration model for the G7 stock markets. Review of Financial Economics. 23 (1): Bahmani-Oskooeea, M., Aftab, M. (2017).On the asymmetric effects of exchange rate volatility on trade flows: New evidence from US-Malaysia trade at the industry level. Economic Modelling. 63:

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