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BY 4.0 license Open Access Published by De Gruyter Open Access October 7, 2022

Nonlinear and Asymmetric Impact of Oil Prices on Exchange Rates: Evidence from South Asia

  • Wenxin Gao , Jun Wen , Muhammad Zakaria and Hamid Mahmood EMAIL logo
From the journal Economics

Abstract

The study analyses the asymmetric and nonlinear influence of oil price on exchange rates of South Asian countries in time–frequency framework using wavelet technique. For empirical analysis, monthly data are examined from July 1983 to June 2018. Wavelet coherence results show that the variables are in phase, i.e., oil prices and exchange rates are positively correlated. It suggests that oil price influences exchange rates positively. The study also investigates the causal association between oil price and exchange rates using a nonlinear causality test. The results of nonlinear causality show that there is bidirectional causality between oil price and exchange in Bangladesh and India and unidirectional causality from oil price to exchange rate in Pakistan and Sri Lanka. The findings provide some important recommendations to investors and policy makers.

JEL Classification: C22; F31; Q43

1 Introduction

Oil is an important energy input for economic development of a country as oil prices affect cost of production especially in oil consuming economies. Oil price affects the macroeconomy and capital markets of the countries. Large increase in oil prices results in economic recession, surge in inflation and trade deficits in oil importing countries. It also increases the uncertainty and stocks and bonds value (Chaudhuri & Daniel, 1998). Oil price also affects exchange rate of both oil producing and consuming countries (Huang & Tseng, 2010).

Like oil, exchange rate is also important for economic growth of a country as it affects tradeable sector and investment level of the country. Fluctuations in real exchange rate (RER) occur mainly due to non-monetary/real shocks like oil prices, fiscal condition, productivity, labor supply, etc. Among these variables, oil price fluctuations is the main variable which is responsible for RER fluctuations (Chaudhuri & Daniel, 1998). Thus, both oil prices and exchange rates are associated in such a way that shocks in oil market can be transferred to exchange rate market.

Theoretically, there are different transmission mechanisms via which oil prices can affect exchange rates, i.e., terms of trade and wealth effect (Beckmann & Czudaj, 2013; Benassy-Quere, Mignon, & Penot, 2007; Habib, Bützer, & Stracca, 2016). According to the terms of trade channel, there are two sectors (tradeable and non-tradeable) in the economy and both sectors employ a tradeable input (oil) and a non-tradeable input (labor). If there is increase in oil price, then labor price should decrease so that tradeable sector may remain competitive. If non-tradeable sector is more energy intensive compared to tradeable sector, then RER will appreciate and vice versa. In oil-exporting countries, high oil prices create positive terms of trade shocks, which results in Dutch disease hypothesis by increasing the price of the non-tradeable goods which appreciates domestic currency. According to Chen and Chen (2007), in oil dependent countries, high oil price worsens terms of trade by increasing price of tradeable goods in the domestic country relatively more than in foreign country, it depreciates the RER of local currency. Further, to maintain its competitiveness, the home country needs to increase nominal exchange rate (NER), which further depreciates RER.

According to the wealth transmission channel, when oil price rises, it transfers wealth from oil-consuming to oil-producing countries. It appreciates (depreciates) the exchange rates of the oil-exporting (importing) countries by improving (deteriorating) their trade balance (Krugman, 1983). According to Golub (1983), high oil price improves (deteriorates) current account balance of oil exporting (importing) countries. If the supply of dollars in oil exporting countries is larger than the demand of dollars in oil-importing countries, then this excess supply of dollar will depreciate dollar and will appreciate non-dollar/foreign currencies. There is also an interest rate channel. According to Darby (1982), high oil price increases inflation. This high inflation increases domestic interest rate, which increases foreign capital inflows. This appreciates local currency.

After the oil crisis of 1973, many studies have investigated the impact of oil prices on different sectors of the economy like GDP, stock market, inflation, investment, exchange rate, terms of trade, interest rate, etc. Some studies also explored the influence of international oil price on exchange rates. These studies have mainly shown that high oil price appreciates (depreciates) the currencies of oil exporting (importing) economies. Section 2 elucidates literature review in detail. The existing empirical literature has mainly examined oil price and currency link for developed economies and oil-producing countries. Few authors have analyzed the case of oil-dependent small open economies. Empirical literature for South Asian countries is almost scant. Due to high economic growth, population growth, modernization, urbanization, industrialization and energy consumption, demand for oil and its related product is increasing in South Asia. The acceleration of economic growth in South Asia has increased its dependence on imported oil. The oil prices have affected the real economy of these countries by affecting different macroeconomic factors. Exchange rate is an important channel via which oil price shock is transferred to the economy. Thus, this study examines the oil price and currency linkages in oil-dependent small open economies of South Asia.

Earlier studies have used traditional econometric techniques to analyze oil price and exchange rate nexus like cointegration, Granger causality tests, the vector autoregressive (VAR) models, the vector error correction model (VECM), correlation, etc. These techniques can examine only the linear effect of oil price on exchange rate, while the actual impact of oil price on exchange rate may be nonlinear and asymmetric. Further, the traditional econometric techniques require stationarity of the variables, but both oil price and exchange rate are not stationary variables. Hence, the present study will explore the asymmetric and nonlinear influence of oil price on exchange rate using wavelet technique. This technique also does not require stationarity of the variables.

The present study bears some important contributions to the empirical literature due to several reasons. First, it will re-examine the effect of oil price on exchange rates as earlier studies have provided the inconclusive impact of oil price on exchange rates. Second, it will explore the causal relationship in time–frequency domain as causality can differ over time and frequency. For this purpose, it will use continuous wavelets tools to explore the effect of oil price on exchange rates. It will help to find nonlinear relationships among both variables. Third, the traditional linear Granger causality tests are ineffective to find nonlinear relations. Therefore, this study will use Diks and Panchenko (2006) nonlinear causality test. Fourth, it will examine oil price and currency linkages in South Asia, where only limited literature is available. Thus, this study is an important contribution to the literature.

The remaining article is laid out as follows. Section 2 elucidates literature review. Section 3 elaborates methodology. Section 4 discusses the estimated results. Conclusion is provided in Section 5.

2 Review of Literature

In literature, several studies have empirically explored the influence of international oil price on exchange rates. Earlier studies by Golub (1983) and Krugman (1983) document that high oil price may appreciate (depreciate) the currencies of oil exporting (importing) economies. Later on, several other studies have also examined oil price and exchange rate linkages. Amano and van Norden (1998) and Chaudhuri and Daniel (1998) assert that oil price fluctuation is the main factor of exchange rate fluctuation in US dollar after Bretton Woods system. Akram (2004) has shown nonlinear impact of oil price on exchange rates in Norway. Earlier literature has basically supported the Dutch disease hypothesis (Issa, Lafrance, & Murray, 2008; Korhonen & Juurikkala, 2009).

Table 1 gives the summary of the key recent studies which have explained the oil price and exchange rate linkages. The table reveals that the studies have shown mixed outcomes regarding the influence of oil prices on exchange rates. In general, the findings reveal that high oil price appreciates (depreciates) the currencies of oil exporting (importing) economies and have validated the Dutch disease hypothesis in oil exporting countries (Al-mulali & Sab, 2012; Hasanov et al., 2017; Korhonen & Juurikkala, 2009; Lizardo & Mollick, 2010; Oomes & Kalcheva, 2007). However, Mohammadi and Jahan-Parvar (2012) have provided limited support for Dutch disease hypothesis. Basher, Haug, and Sadorsky (2012) have shown that exchange rate of emerging economies become weak after oil price increase. Using data from 43 countries, Habib et al. (2016) have documented that there is no systematic association between oil price and exchange rates. Some studies have also shown insignificant association between both variables (Habib & Kalamova, 2007). According to Reboredo (2012), association between oil price and exchange rates is weak.

Table 1

Review of literature

Studies Country of analysis Data period Exchange rate variable Estimation method Key finding(s)
Habib and Kalamova (2007) Norway, Russia and Saudi Arabia 1980Q1–2006Q2 REER OLS, Johansen cointegration and VECM • There is a long run association among oil prices and exchange rates in Russia but not in Norway and Saudi Arabia
1995Q1–2006Q2 (Russia)
Benassy-Quere et al. (2007) USA January 1974 to November 2004 RER and REER Cointegration and causality • The findings reveal that high oil prices appreciate dollar
Chen and Chen (2007) Panel of G7 countries January 1972 to October 2005 RER Cointegration • High oil price depreciates exchange rates in the long run
FMOLS, DOLS and PMG
Huang and Guo (2007) China January 1990 to October 2005 RER SVAR • Hiked oil prices appreciate exchange rate in long run
Narayan, Narayan, and Prasad (2008) Fiji 11/29/2000–9/15/2006 NER GARCH and EGARCH • High oil prices appreciate the Fijian dollar
Korhonen and Juurikkala (2009) Nine OPEC countries 1975–2005 RER and REER Pool mean group estimator • High oil price appreciates exchange rate
Lizardo and Mollick (2010) Eight countries Data varies for each country NER Johansen cointegration • High oil prices appreciate (depreciate) currencies of oil exporting (importing) economies
Ghosh (2011) India 02/07/2007–28/11/2008. NER GARCH and EGARCH • High oil price depreciates Indian exchange rate against US dollar
• The effect of oil price shocks on exchange rate volatility is permanent
Mendez-Carbajo (2011) Dominican 1990–2008 RER VAR, VECM and granger causality • High oil price depreciates exchange rate
Mohammadi and Jahan-Parvar (2012) 13 Oil-exporting countries January 1970 to January 2010 RER TAR and M-TAR cointegration • Oil price has long run influence on exchange rate
• No causality is found between variables
• There is limited evidence for Dutch disease
Basher et al. (2012) Emerging economies January 1988 to December 2008 Trade-weighted exchange rate index in US dollar SVAR • Oil prices play a main role to determine exchange rate in emerging countries
• High oil prices have weakened the exchange rate in these countries
Al-mulali and Sab (2012) Panel data for 12 oil-exporting countries 2000–2010 RER Random effect model • The results show that high oil price appreciates the exchange rate
Wu, Chung, and Chang (2012) USA 02/01/1990–28/12/2009 US dollar index futures Copula-based GARCH • The linkages among oil prices and exchange rates are negative and has decreased after 2003
Reboredo (2012) USA 04/01/2000–15/06/2010 Trade weighted exchange index Correlation, Copula, ARMA and TGARCH • There is weak association among oil price and exchange rates
Turhan, Hacihasanoglu, and Soytas (2013) 13 Emerging economies 03/01/2003–02/06/2010 NER VAR • Oil prices have depreciated the exchange rates after global financial crisis
Ahmad and Hernandez (2013) 12 major oil-exporting and oil-importing countries January 1970 to January 2012 RER TAR and M-TAR • Oil price has asymmetric impacts on exchange rate
Chen et al. (2013) Philippines 1970Q1–2011Q4 RER Engle–Granger cointegration test, TAR and MTAR • There is an asymmetric association among oil prices and exchange rates
Tiwari et al. (2013a) India April 1993 to December 2010 REER Wavelet • Oil prices and exchange rates are not linked at lower scale. At higher scale there is bidirectional causality between both variables
Tiwari, Mutascu, and Albulescu (2013b) Romania February 1986 to March 2009 REER Wavelet • Oil price has short and long run impact on exchange rates
Aloui, Ben Aïssa, and Nguyen (2013) US 04/01/2000–17/02/2011 NER Copula-GARCH • High oil price depreciates dollar
Brahmasrene, Huang, and Sissoko (2014) US January 1996 to December 2009 NER Cointegration, VAR and granger causality • Oil price shocks have an important influence on exchange rate both in medium and long run
Fowowe (2014) South Africa 02/01/2003–27/01/2012 NER return GARCH • High oil price depreciates South African currency
Bal and Rath (2015) China and India January 1994 to March 2013 REER Hiemstra and Jones (1994) nonlinear granger causality test • There is a bidirectional (unidirectional) causality among oil price and exchange rate in India (China)
Bouoiyour, Selmi, Tiwari, and Shahbaz (2015) Russia 1993Q1–2009Q4 REER ARDL and wavelet • The results show that causality goes from oil price to exchange rate at lower frequency
Pershin, Molero, and Gracia (2016) Botswana, Kenya and Tanzania 01/12/2003–02/07/2014 NER Cointegration and VAR • Oil price shock has different impacts on exchange rate of these countries
Shahbaz et al. (2015) Pakistan February 1986 to March 2009 REER Wavelet and ARDL • Causality among oil price and exchange rate is different at different time scales
Ngoma, Ismail, and Yusop (2016) Egypt, Ghana, Nigeria, South Africa and Tunisia May 1960 to April 2014 (Egypt) RER Cointegration and error correction • A long run relationship is found among oil price and exchange rate
January 1990 to December 2013 (Ghana) • In short run, oil prices appreciate exchange rates
January 1985 to April 2013 (Nigeria)
January 1970 to April 2014 (South Africa)
January 1998 to April 2014 (Tunisia)
Su, Zhu, You, and Ren (2016) Australia, Canada, the European Union, Japan, Mexico, Norway and UK January 1974 to March 2015 RER return Quantile regression • Oil shocks have heterogeneous impacts on exchange rates across quantiles
Hasanov, Mikayilov, Bulut, Suleymanov, and Aliyev (2017) Azerbaijan, Kazakhstan and Russia 2004Q1–2013Q4 RER ARDL • Oil price appreciates exchange rate in these economies
Kim and Jung (2017) Currencies of 40 countries 03/01/2013–06/10/2014 NER FDA and copula • High oil prices depreciate most of the currencies
Yang, Cai, and Hamori (2017) Eight Oil producing and oil consuming countries 01/01/1999–31/12/2014 NER Wavelet • Oil price negatively affects exchange rate in oil-exporting economies, while it has uncertain effects on exchange rate of oil importing economies
Kisswani, Harraf, and Kisswani (2019) ASEAN-5 1970Q1–2016Q4 Growth rate of RER Nonlinear ARDL • For some countries, a bidirectional and for other countries a unidirectional causality holds among oil-price and exchange rate
• Results also show asymmetric effect for Indonesia and Malaysia

These inconclusive findings are due to several reasons. First, different studies have taken different sample countries which vary in their features like oil producing, oil consuming, developing, developed, emerging countries, etc. Second, different studies have used different econometric techniques for the analysis which have yielded different results. Third, studies have used different time period, data type (panel vs time series), data frequency (monthly, quarterly and annual), etc. for the analysis. Fourth, results are also different as different studies have used different (nominal vs real) exchange rates and different oil price measures.

It is apparent from the table that studies mainly concentrated on oil exporting and developed countries and ignored the analysis for small open economies. Only scarce literature is available for oil-dependent small open economies like South Asian countries. Ghosh (2011), using daily data from July 2007 to November 2008, has examined the influence of oil price shocks on Indian exchange rate. The study applied GARCH and EGARCH techniques for the analysis. The findings reveal that oil price shocks have depreciated Indian currency against US dollar. Later on, Tiwari, Dar, and Bhanja (2013a) have shown linear and nonlinear association among oil prices and Indian currency. Shahbaz, Tiwari, and Tahir (2015), using data from February 1986 to March 2009, have explored the linkages among oil prices and exchange rates in Pakistan using wavelet technique. The findings reveal that the causality among oil prices and exchange rates vary across scales. No study is available for any other South Asian country. This study will fill the gap in empirical literature by exploring the nonlinear and asymmetric impact of oil prices on exchange rates of South Asian countries by using wavelet technique. Further, nonlinear causality test of Diks and Panchenko (2006) will also be applied for robustness analysis.

Studies have mainly used traditional econometric techniques like linear regression, correlation, cointegration, Granger causality, VAR, ARDL model, GARCH, etc., to find oil price and currency linkages. Few recent studies have used sophisticated techniques like wavelet analysis, frequency domain analysis, copulas, nonlinear techniques, etc. The traditional cointegration tests are based on the assumption that the response of exchange rates to oil price shocks is symmetric. However, the response of exchange rates to oil price shocks is asymmetric. Therefore, some studies have documented that oil prices have nonlinear and asymmetric impact on exchange rates (Ahmad & Hernandez, 2013; Chen, Lee, & Goh, 2013; Kisswani et al., 2019; Tiwari et al. 2013a).

3 Theoretical Framework

To gauge the interdependence among oil price and exchange rate, both in time and frequency, wavelet technique will be used. The word wavelet means a small wave. Small means that a function is of limited length and the wave means that the function is of oscillatory type. The continuous wavelet transformation (CWT) is specified as follows (Rua & Nunes, 2009, Vacha & Barunik, 2012):

(1) V x ( u , c ) = x ( t ) 1 c φ t u c d t ,

where V x ( u , c ) is estimated for the wavelet φ ( . ) for a specific time series. 1 c is the parameter to get unit variance of wavelet || φ u , c || 2 . It is a normalization factor so that the wavelet transformations may become comparable across scales and time. u ( c ) is the location (scale dilation) parameter of the wavelet. In simple words, u determines the time position and c is the scale. Scale is inversely related to frequency, where low scales explain quick changes (high frequencies), while high scales explain slow changes (low frequencies).

To measure variance at different frequency levels, wavelet power spectrum is calculated as | V x ( u , c ) | 2 . It measures the variance of series both in time and scale. To demonstrate the correlation between the series, wavelet coherence is used.

(2) R 2 ( u , c ) = | ( V xy ) | 2 ( | V x | 2 ) ( | V y | 2 ) ,

where is a smoothing operator. Smoothing is important, as without smoothing coherency would always be equal to 1. R 2 ( u , c ) falls between 0 and 1, i.e., 0 R 2 ( u , c ) 1 . Value close to zero (one) indicates weak (strong) correlation between variables in time and across frequencies.

Wavelet coherence cannot distinguish between positive and negative dependence structure as it is squared. Therefore, to get the direction of causality between the series, phase difference tool is used. The phase difference P x , y between x ( t ) and y ( t ) is represented as follows (Aguiar-Conraria, Azevedo, & Soares, 2008)

(3) P x , y = t a n 1 I ( V xy ) R ( V xy ) ,

where I ( R ) is imaginary (real) part of a complex number. Two series will co-move when phase difference is zero at the specified frequency. Further, if the series is in phase (positively correlated), the arrow will be oriented to right. In this case y leads x when P x , y [ 0 , π 2 ] , and x leads y when P x , y [ π 2 , 0 ] . The series is said to out of phase (negatively correlated), when the phase difference is π or π . In this case, the arrows will be pointed to left. If P x , y [ π , π 2 ] , then y is leading, and if P x , y [ π 2 , π ] , then x is leading. Moreover, if arrows point left and up or right and down, then y leads x . If arrows point left and down or right and up, then x leads y .

Wavelet-based causality is mesaured using continuous wavelet transform without spectral matrix factorization (Olayeni, 2016), which is built on wavelet correlation method (Rua, 2013). The wavelet correlation method (Rua, 2013) is explained as

(4) ρ xy ( u , c ) = R ( V xy ( u , c ) ) | V x ( u , c ) | 2 | V y ( u , c ) | 2 .

ρ xy ( u , c ) falls between −1 and 1, i.e., 1 < ρ xy ( u , c ) < 1 . This correlation method explains co-movements of the series both in time and frequency simultaneously. The wavelet causality measure is provided as

(5) C x y ( u , c ) = R ( V xy ( u , c ) ) I x y ( u , c ) | V x ( u , c ) | 2 | V y ( u , c ) | 2 ,

where I x y ( u , c ) is an indicator function such that

(6) I x y ( u , c ) = 1 , if ψ xy ( u , c ) ϵ ( 0 , π / 2 ) ( π , π / 2 ) 0 , otherwise .

4 Estimated Results

4.1 Data and Basic Results

For analysis, monthly data for oil price and RER are taken for the flexible exchange rate period from July 1983 to June 2018 for four South Asian countries namely Bangladesh, India, Pakistan and Sri Lanka. Data of Dubai Fateh oil price (US dollar per barrel) is taken from OPEC. RER is constructed as NER adjusted for domestic and foreign price levels (measured by consumer price index; CPI). NER is local currency per unit of US dollar and is taken in direct quotation in which a high numerical value of currency indicates depreciation of local currency. Data for NER, and domestic and foreign (US) CPI are taken from International Financial Statistics.

Table 2 gives some basic data statistics. The average value of oil price is $40.84 per barrel, while the minimum is $6.95 and the maximum is $131.22. The standard deviation is 31.54. The RER for Bangladesh has a mean of 75.71, with a minimum of 54.46 and a maximum of 165.94 and the standard deviation is 15.50. The average real value of Indian rupee is 53.44, which ranges between 40.21 and 71.09 with the standard deviation of 7.57. Similarly, the real average value of Pakistani rupee is 81.49, which ranges between 62.36 and 109.31 with standard deviation of 9.48. The RER for Sri Lanka has a mean value of 147.09, which ranges between 72.53 and 203.97 with standard deviation of 26.18. All skewness values are positive which show that the variables are skewed right. All kurtosis coefficients are also positive, which indicate that all series exhibit leptokurtic behavior. It implies that the distribution of series has larger, thicker tails as compared with normal distribution. Statistically significant values of Jarque–Bera (JB) statistics show that variables do not have normal distribution. Section 4.2 provides the wavelet analysis. For wavelet analysis, real oil price data are used, which are constructed as crude oil price of Dubai Fateh deflated by CPI of US. Before analysis, both real oil price and RER are converted to first log differences.

Table 2

Descriptive statistics

C RERs
Oil price ($) Bangladesh India Pakistan Sri Lanka
Mean 40.84 75.71 53.44 81.49 147.09
Median 26.04 73.58 53.21 79.16 149.70
Minimum 6.95 54.46 40.21 62.36 72.53
Maximum 131.22 165.94 71.09 109.31 203.97
Std dev. 31.54 15.50 7.57 9.48 26.18
Skewness 1.11 2.20 0.05 0.55 0.01
Kurtosis 2.96 10.16 1.69 2.63 2.16
Jarque–Bera (JB) 85.72** 1235.83** 30.16** 23.76** 12.35**
Probability (JB) 0.00 0.00 0.00 0.00 0.00

Note: ** indicates statistical significance of value at 1% level.

4.2 Wavelet Spectra

CWT power spectral plots of oil price and exchange are shown in Figure 1. CWT plots basically show the power/variance of the series. Oil price plot indicates that it has high fluctuations between 1984 and 1986 at 0–32 months of scale (high to medium frequency), 1988–1991 at 6–14 months of scale and 2007–2009 at 8–26 months of scale frequency.[1] Thus, oil price shows high fluctuations in three periods, but at different frequencies. In the first two periods, oil price decreased globally and in the third period, oil price surged and this period is also the period of 2007/08 financial crisis. Bangladesh exchange rate has strong power in short and medium run at 1–32 months of scale during 1983–1988. Volatility in Indian exchange rate is scattered throughout the time period at different frequency levels. Exchange rate in Pakistan has high fluctuations between 1995 and 2004 at 4–24 scales (high to medium frequency) and between 2007 and 2010 at 6–20 scales (high to medium frequency). There is volatility in Sri Lankan exchange between 1997 and 1999 at frequency level of 1–16 months.

Figure 1 
                  CWT power spectrum of (a) oil price and (b) exchange rates. Note: Significance level at 5% is shown as thick black contour. Blue (red) color shows low (high) power. X-axis provides time period, while Y-axis provides frequency (in months).
Figure 1

CWT power spectrum of (a) oil price and (b) exchange rates. Note: Significance level at 5% is shown as thick black contour. Blue (red) color shows low (high) power. X-axis provides time period, while Y-axis provides frequency (in months).

Wavelet coherency (WTC) plots of oil prices with exchange rates are depicted in Figure 2. For WTC of oil prices with exchange rate of Bangladesh, co-movements are mainly found at frequency range of 28–36 months during 1986–1990, at scale of 16–64 months during 1993–2010, and at scale of 64 months onwards during 2002–2018. The arrows are mainly oriented to right thus variables have positive relationships. For India, co-movements are found at different frequencies from 2000 to 2010. The main co-movement is observed during 2003–2010 at 18–40 months band. For Pakistan, co-movement is found between 1995 and 2012 mainly at 8–16 months frequency and at 2–32 months frequency. As for as Sri Lanka is concerned, there are co-movements between oil price and exchange rate at different frequency levels at different time periods but the main co-movement between both the series is found at 36–68 months frequency during 1990–2010 time periods. The arrows point to right and up, it shows that oil price has positive influence on exchange rate.

Figure 2 
                  WTC between oil prices and exchange rates. Note: Significance level at 5% is shown by thick black contour which is calculated using Monte Carlo simulations.
Figure 2

WTC between oil prices and exchange rates. Note: Significance level at 5% is shown by thick black contour which is calculated using Monte Carlo simulations.

Wavelet causality results are provided in Figure 3. The color code indicates the strength of causality and it goes from 0 to 1. For Bangladesh, a strong causality among oil price and exchange rate is observed between 2003 and 2010 on 20–32 months scale and between 1998 and 2008 on 64 months onward frequency. This causality is little bit weak in India and Sri Lanka and is strong in Pakistan where a strong causality holds between 2006 and 2011 at 4–16 months. Causality also holds at different time periods and at different frequency levels. Our results support the findings of Tiwari et al. (2013a), which also did not find any causality between oil price and Indian currency at low time scales.

Figure 3 
                  Wavelet-based causality from oil price to exchange rate. Note: Statistical significance at 5% (10%) is shown through white (red) contours.
Figure 3

Wavelet-based causality from oil price to exchange rate. Note: Statistical significance at 5% (10%) is shown through white (red) contours.

Figure 4 reports the Rua (2013) measure of CWT correlation. It is evident from these figures that variables are positively correlated during the same period as shown in WTC plots given in Figure 2. These results generally support the findings of WTC. The first figure gives the correlation among oil price and exchange rate in Bangladesh. Both variables are positively correlated during 1995–2001 at 8–24 months scale (high and medium frequencies) and 2002–2010 at 0–32 months band. This positive co-movement also holds for most of the time period at low frequencies. In India, the correlation between series is observed at high to medium frequency 0–24 months scale but at different time periods. The main strong correlation is found during 2006–2010 at 5–16 months scale. For Pakistan, correlation is found between 1993 and 2003 at 6–24 months scale and between 2006 and 2011 at 2–16 months scale. Correlation is also found at low frequency from 1983 to 2004. In Sri Lanka, correlation among oil price and exchange rate is found at different frequency levels and at different time periods. Correlation holds during 1989–1993 at 4–16 months frequency level and during 2004–2010 at 2–16 months scale. A very strong correlation between both series is found during 1993–2018 at 32–100 months frequency (low frequency). It indicates that oil price has affected South Asian currencies. These correlation results support the notion that oil price fluctuations are positively associated with exchange rates in South Asia.

Figure 4 
                  Wavelet-based correlations (Rua, 2013) between oil price and exchange rates. Note: The color code shows strength of correlation. Blue (read) color indicates negative (positive) correlation between variables.
Figure 4

Wavelet-based correlations (Rua, 2013) between oil price and exchange rates. Note: The color code shows strength of correlation. Blue (read) color indicates negative (positive) correlation between variables.

4.3 Results of Causality Test

To endorse the findings of the wavelet estimates, the study also applies nonlinear causality test of Diks and Panchenko (2006) to find causality among oil prices and exchange rates. First, we have checked the stationary properties of the variables. Table 3 shows the results of the unit root tests. These results reveal that all series are non-stationary at levels and are stationary at first differences. Table 4 provides the results of nonlinear causality test. The results reject the null hypotheses of no causality from oil price to exchange rates, which suggests the presence of nonlinear causality from oil price to exchange rates in South Asia. Nonlinear causality test in case of Bangladesh and India also rejects the null hypothesis of no causality from exchange rate to oil prices. It indicates the presence of bidirectional causality between oil prices and exchange rate in Bangladesh and India and unidirectional causality between oil prices and exchange rates in Pakistan and Sri Lanka. These results show that oil price influences the exchange rates of oil importing economies of South Asia. Thus, governments in these countries need to take appropriate steps to stabilize their exchange rates.

Table 3

Stationarity tests

Zivot and Andrews (1992) Lee and Strazicich (2003) Perron (1989)
Level First difference Level First difference Level First difference
Oil price −4.54 −10.85* −1.98 −10.49* −4.50 −10.83*
Bangladesh −4.73 −12.00* −1.65 −10.16* −4.72 −14.81*
India −4.45 −10.43* −2.10 −7.04* −4.72 −18.41*
Pakistan −4.34 −18.19* −2.20 −3.86* −4.33 −19.13*
Sri Lanka −4.77 −15.78* −2.98 −14.87* −4.98 −22.41*

* implies p < 0.05 . Critical value at 5% of the Zivot and Andrews test is −4.93, Lee and Strazicich is −3.347 and Perron test is −5.23.

Table 4

Causality estimates

Null hypotheses ( H 0 )
Oil price does not influence exchange rate Exchange rate does not influence oil prices
t-statistics P-value t-statistics P-value
Bangladesh 2.570 0.005** 1.824 0.034*
India 1.915 0.027* 1.869 0.030*
Pakistan 3.256 0.000** 0.908 0.181
Sri Lanka 2.417 0.007** 1.272 0.101

**(*) implies p < 0.01 ( 0.05 ) .

5 Conclusion

The study examines the association between oil price and exchange rates of South Asian economies using data from July 1983 to June 2018. For empirical analysis, wavelet coherence framework is used as it provides relationship among series in time–frequency space. The findings indicate that oil price and exchange rates are positively correlated. It shows that high oil price depreciates South Asian currencies. Being oil importing economies, this result is in line with theoretical expectations. The results of nonlinear causality show that there is bidirectional causality among oil price and exchange rate in Bangladesh and India and unidirectional causality between oil price and exchange rate in Pakistan and Sri Lanka.

These results have some useful policy recommendations for investors and policy makers. Monetary authorities in South Asian countries need to consider the role of oil price shocks in determining exchange rate dynamics. To stabilize the exchange rates after oil price shocks, monetary authorities should formulate and implement policies in such a way that the impact of oil price shocks on exchange rates is minimized. Monetary authorities may concentrate on inflation target to offset exchange rate instability. Further, South Asia countries should increase domestic production of oil and look for other energy sources to prevent depreciation of their currencies. High oil price can misalign the exchange rates. The flexible exchange rate system of South Asian countries can make their economies susceptible to changes in oil price shocks, as in flexible exchange rates system high oil price produces greater volatility in exchange rate compared to fixed exchange rate system. Therefore, these countries may use managed flexible exchange rate system to avoid exchange rate volatility after oil price shocks.

  1. Funding information: This research has not been funded.

  2. Conflict of interest: Authors state no conflict of interest.

  3. Article note: As part of the open assessment, reviews and the original submission are available as supplementary files on our website.

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Received: 2022-01-07
Revised: 2022-07-18
Accepted: 2022-09-12
Published Online: 2022-10-07

© 2022 Wenxin Gao et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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