Do prices of crude oil and other fossil fuels influence real exchange rates in ASEAN countries?

Abstract

We examine the influence of ASEAN countries’ crude oil prices on real exchange rates against the US. There is some evidence that higher oil prices impose additional pressure on developed nations (non-US) as exporters and importers through the exchange rate channel. Our findings suggest that a short-term increase in the prices of oil, gas, and coal also induces an added burden on ASEAN countries because higher fossil fuel prices depreciate local currencies. Price variations in natural gas do the most damage through the exchange rate channel. We also find oil and coal effects are weaker during financial crisis periods. Gas price effects are stronger during financial crises. In the long-run, higher energy prices depreciate several ASEAN countries’ currencies, with Vietnam showing the converse with respect to some energy prices. We compare each nation’s competitiveness against their energy mix with other ASEAN members. We explain the results and indicate policy implications.

(150 words)

JEL classification: E31; F31; Q43

Keywords: Fossil fuel prices; Real exchange rate returns; ASEAN; Financial crisis.

1. Introduction

The US dollar is the major invoicing and settlement currency in the international market for crude oil.

Theoretically, a higher oil price increases demand for the US dollar and this increases the exchange rate

(defined as the US dollar expressed in terms of (non-US) importer/exporter currency). More (non-US)

importer/exporter currency is needed to buy the US dollar, which in turn depreciates the (non-US)

importer/exporter currency. This suggests that higher (lower) oil price reduces (increases) price

competitiveness. Studies of selected OECD countries show that this relationship does exist (Amano and

van Norden, 1998; Camarero and Tamarit, 2002; Chen and Chen, 2007; Lizardo and Mollick, 2010).

The authors found only four studies of non-OECD countries, three are single country based studies,

namely, the Ghosh (2011) study on India and Ju et al. (2014) on China, both of which show a negative

effect; and Narayan et al. (2008) on Fiji, showing a positive effect. One study based on selected Asian

countries shows that there are some nations that show appreciating effects; some show depreciation; and

others show no effects of this currency relationship (see Narayan, 2013). The implication of oil price

variations on exchange rate is at best unclear.

The aim of this study is to examine this link between crude oil price variations and price

competitiveness and compare it against two other fossil fuels, natural gas and coal. The Annual Energy

Outlook 2015 (EIA, 2015) implies significant medium to long term shifts in the consumption and

production pattern of traditional fossil fuels, which currently supply up to 80% of the world’s energy

(EIA, 2015). Total primary energy consumption is forecast to grow from 97.1 quadrillion btu in 2013 to

105.7 quadrillion btu in 2040 (EIA, 2015). However, the EIA (2015) predicts that this growth will be

driven by consumption of natural gas and renewable energy, and not the largest source of energy

worldwide—crude oil. Consumption of petroleum products is forecast to remain at 2013 levels in 2040,

as fuel consumption in the transportation sector (one of biggest consumers of petroleum) declines as a

result of a 70 per cent increase in the average efficiency of on-road light-duty vehicles and increased use

in vehicles of compressed natural gas, liquefied natural gas (LNG) and other liquids (EIA, 2015).

Natural gas, in particular, is quickly emerging as one of the major energy sources because it is much

cleaner than oil or coal. Increased natural gas production from new sources, such as the US, is likely to

see its price falling, making it more economically competitive (EIA, 2013; 2015). On the other hand,

coal, the world’s second largest energy source, is still a cheaper source of energy than oil and gas.

Nonetheless, coal’s price is expected to increase, but at a decelerating rate, as a result of policies and

regulations encouraging a switch to cleaner energy sources (EIA, 2015).

As these forecasts herald significant changes in the composition of the energy mix, and the price

competitiveness of the three fossil fuels over time, we ask how changes in fossil fuel prices impact the

exchange rate. Exchange rate movements are critical to determining price competitiveness of a nation’s

trade with the rest of the world. Our study is therefore concerned with the size of price competitiveness

determined through the exchange rate and how this differs across these three fossil fuels.

We test for the real price competitiveness of three fossil fuels (oil, coal, and gas) through the

exchange rate channel over the period 1992:1 to 2013:12. To do this we examine the US dollar against

the ten countries of the Association of Southeast Asian Nations (ASEAN), namely, Brunei, Cambodia,

Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam.1 Over the next

few decades this group of diverse countries is set to become one of the driving forces behind the

increase in energy demand. Covering 4.34 million square kilometres, equivalent to 3.3% of the world’s

total land area2 and with a total population of 633.35 million3, energy demand from the ASEAN

members alone is expected to increase by over 80% between 2013 and 2040 (see, IEA, 2013, 2015).

To meet their growing energy demand and supplement the region’s declining production of crude oil

in maturing oil fields, the ASEAN members are increasingly becoming reliant on imports of crude oil.

Gas and coal are also imported by most members, although for two countries, namely the Philippines 1 In 2013 the ASEAN members had a combined Gross Domestic Product (GDP) of US$5.7 trillion, or 5.6% of the world economy in purchasing power parity terms (World Bank, 2015). The ASEAN includes a mix of exporters and importers of fossil fuels, with differing patterns and scales of energy consumption and fossil fuel endowments (we explain this in Section 3).2 The Institute of Energy Economics (IEE), Japan, (2011), see http://eneken.ieej.or.jp/en/.3 2012 figure: This is extracted from: http://aseanup.com/asean-infographics-population-market-economy/.

and Vietnam, total production can be consumed locally (see Table 1). Indeed, the ASEAN members are

also looking at regional cooperation in natural gas and coal—energy resources that they are endowed

with. For instance, under the ASEAN 2010-2015 Plan of Action for Energy Cooperation (APAEC),

members have cooperated to develop the trans-ASEAN gas pipeline, a total of 12 interconnected

bilateral pipelines with a total length of 3377km. The new APAEC for 2016-2025 seeks to further

enhance cooperation in regard to fossil fuels and renewable energy.4 Further, since coal is still relatively

more competitive and abundant5 in the region than natural gas; the ASEAN members’ policies are now

geared towards pushing for clean coal technology and improving coal imports within ASEAN. Members

are also discussing nuclear energy, as well as increasing the share of renewable energy (wind, hydro,

and solar) in their energy mix.

Nonetheless, with the role of imported fossil fuel unlikely to diminish anytime soon— especially

with oil prices becoming more affordable and gas related products becoming increasingly competitive—

we check the relative standing of the three fossil fuels in terms of their effects on their currencies against

the US dollar over a period of 21 years, up to the end of 2013. This has implications for energy traders

as well as other domestic importers and exporters through the exchange rate channel.

Foreshadowing our key results, we find a less than proportional but significant effect of fossil fuel

prices on the local ASEAN exchange rates against the US. The sign effects are the same across this

group—higher (lower) prices of all three resources lead to a depreciation (appreciation) of local

currencies against that of the US—which signifies shortfalls (gains) to traders of fossil fuel. As higher

fuel prices depreciate local currency against the US dollar, importers are bound to make losses in

currency conversions. These losses differ significantly between the ASEAN members. The sign effects

are found to be robust to models fortified with other determinants of real exchange rate and events, such

as the Asian financial crisis (AFC), the bursting of the dot.com bubble, and the global financial crisis

4 Source: aseanenergy.org.5 EIA (2013).

(GFC). The size effects and their significance are found to change with the inclusion of additional

determinants of real exchange rates.

We also find that the effects of fossil fuel prices on real exchange rate (RER) returns are asymmetric

during periods of financial crisis and non-financial crisis. There is a tendency for the effects of oil and

coal prices to be weaker (stronger) during bad (good) times. On the other hand, gas prices are found to

have a stronger influence on exchange rates during bad times than good times. The asymmetry is also

strongest in the case of gas price.

The rest of this paper is organised as follows. The following section reviews the literature. Section 3

presents the recent trends in production, consumption, net exports and imports of oil, gas and coal.

Section 4 explains the empirical methods which include unit root and cointegration tests as well as long-

run and short-run regression estimations. Section 5 explains the data. Section 6 discusses the empirical

results, starting with the cointegration results and the long-run influence of the three energy prices (or an

index of the three prices) on real exchange rate returns. This is followed by a discussion of the short-run

results. Section 7 provides a summary of the key findings and concludes the study.

2. Literature on the link between real exchange rates and fossil fuel energy prices and our

contributions

Since the US dollar is the major invoicing and settlement currency in the international market, higher

(lower) energy prices will increase (reduce) demand for the US dollar. In return, increased (reduced)

demand for the US dollar should lead to depreciation (appreciation) of the currency of (non-US)

importers of energy sources against US currency.6

Further, if a higher price of crude oil occurs simultaneously with higher demand for oil by a non-US

importer, the effect can be a much higher depreciation for the non-US importer currency as its currency

6 If the focus is on the exchange rate of the exporter vis á vis the importer, then theory suggests transfer of wealth between the exporter and importer due to oil price changes. Higher (lower) prices may see an appreciation (depreciation) of the importer currency against that of the exporter (for this theory, see Krugman, 1983; Golub, 1983; Corden, 1984; and De Grauwe, 1996).

will suffer a larger depreciation against the US dollar. Narayan (2013) discusses and tests this second

scenario using the case of some Asian nations that subsidise fuel purchases but finds—even with an

artificially higher demand for oil created by government subsidies in some Asian nations—an increase

in oil prices predicts a mixed reaction on local Asian currencies. Vietnam, despite its high fuel subsidies,

was predicted to see an appreciation of its local currency against the US dollar. Our long-run result

seems to confirm this result for Vietnam, but in the short-run, we are able to show the depreciating

effects of higher oil prices.

Lizardo and Mollick (2010) show that currencies of crude oil importers, such as Japan, suffer

depreciation relative to the US dollar when real crude oil prices increase. Other studies also find this

depreciating effect of higher oil price for the currencies of other industrialised nations against the US

dollar (Amano and van Norden, 1998; Camarero and Tamarit, 2002; Chen and Chen, 2007; Lizardo and

Mollick, 2010).7 This outcome is echoed by a study examining the short-run response of RER to oil (see

Chen and Chen, 2007 on G7 countries).8

While the case for selected rich nations confirms the theory described above, non-OECD countries

tend to show mixed results. Ju et al. (2014) find that oil shocks over the period 1983 to 2012 negatively

affected the exchange rate of China. Ghosh (2011) finds that a higher oil price led to a depreciation of

the Indian rupee vis-á-vis the US dollar over the period July 2, 2007 to November 28, 2008. Narayan et

al. (2008), in a study of Fiji, show that higher oil prices led to a short-term appreciation of the Fiji dollar

vis á vis the US dollar over the period 2000-2006. Narayan (2013) finds that for selected Asian nations

oil price is a good in-sample predictor of their exchange rate against the US, although, for some of these

7 Amano and van Norden (1998), for instance, find that higher oil prices led to appreciation of the US effective exchange rate against 15 other industrialised countries. Chen and Chen (2007) find that positive oil price shocks led to the appreciation of the US dollar against the currencies of other G7 nations. Chen and Chen also account for the impact of other theoretically important determinants of exchange rate, including productivity differential and interest rate and find no change in results. Similarly, Camarero and Tamarit (2002) show that higher oil prices led to appreciation of the Spanish currency against a group of EU countries.8 Basher et al. (2012) show support for the hypothesis that exchange rates respond to movements in crude oil prices in the short-run. Similarly, Chen and Chen (2007) find that real oil prices are good predictors of real exchange rates of G7 countries.

countries, a higher oil price was predicted to appreciate the local currency against the US; while for

others, depreciating, and even no significant, effects are also possible.

Some studies examine the possibility of a bidirectional link between oil price and exchange rate.

Bloomberg and Harris (1995) explain that the higher purchasing power of importing nations resulting

from a depreciation of the US dollar can induce an increase in the oil price in US dollar terms. 9 In other

words, the possibility of endogeneity cannot be ruled out. In this current study we test for this

endogeneity and find limited evidence of this suggested feedback effect for ASEAN members.

Further, productivity, inflation, and interest rate differentials are seen as important determinants of

real exchange rate according to theory (the uncovered real interest rate parity and the well-known

Balassa-Samuelson model) and/or various empirical studies.10 With the exception of Chen and Chen

(2007), the focus has mostly been on the random walk exchange rate model when examining the link

between energy prices and exchange rate. We follow the broader exchange rate literature to augment

this model to one that includes the productivity and real interest rate differential.11

We also contribute to this literature by examining the influence of financial crises, in particular the

Asian financial crisis (AFC) of 1997:07 to 1998:12, the bursting of the dot.com bubble of 2001, and the

GFC of 2007:02 to 2009:12. Some studies suggest asymmetric reactions before and after the GFC

(Reboredo, 2012; and Reboredo and Rivera-Castro, 2013) or asymmetry during good and bad times

(Khalifa et al., 2015) in real exchange rates.12 Following these studies, we investigate the presence of

asymmetry in the link between fossil fuel price-exchange rate during financial crises and non-financial

crisis periods. Essentially, if economic growth drives energy consumption, as suggested by the 9 Also see Krichene, 2005; Akram, 2009; Reboredo and Rivera-Castro, 2013; and Salisu and Mobolaji, 2013 for empirical evidence10 See Meese and Rogoff (1988); Chen and Chen (2007); Junttila and Korhnen (2011); Reboredo (2012); Jackman et al. (2013); and Reboredo and Rivera-Castro (2013).11 These additional variables appear in the short-run models in first differenced form with a sufficient lag structure.12 The presence of endogenous or exogenous structural breaks in exchange rates is also suggested (see Narayan, 2008; Narayan et al., 2009; Holmes et al., 2012), and non-linearity in exchange rate (Narayan and Narayan, 2007; Coudert et al., 2011; Kim, 2012; Lee and Chou, 2013). Similarly, Junttila and Korhonen (2011) show inflation differentials as the driving force for the non-linear relationships in the monetary models of the exchange rates of some OECD nations, while Jackman et al. (2013) shows a nonlinear reaction of the foreign exchange market to interest rate differentials. For brevity, and to keep results comparable across countries and fossil fuels, we chose to only use linear modelling. Future research may focus on nonlinear comparisons.

conservative hypothesis, then in times of a financial crisis, with its significant slowdown of economic

activity, the effects of energy prices on real exchange rate can be expected to weaken during a crisis

period, not prior to it.13

Our contribution to this stream of the literature comes from focusing on financial crises which

include the Asian financial crisis, the bursting of the dot.com bubble, and the GFC while the literature

has focused on expansions and contractions (Khalifa et al., 2015) or the GFC (Reboredo, 2012; and

Reboredo and Rivera-Castro, 2013). Further, unlike other studies, we check how this story plays out

between the three fossil fuels.

Our study captures oil, coal, and natural gas prices together to examine the influence of the prices of

fossil fuel on the exchange rates of ASEAN countries. Two recent studies on natural gas (and coal) and

exchange rates are found. Khilafa et al. (2015) find a positive link for all four OECD currencies and

difference in the intensity of the impact of oil and gas between expansionary and contractionary phases

of business cycles, suggesting that the asymmetry is more strongly present in oil than gas. Smiech and

Papiez (2013) suggest that coal price does not influence the Euro against the US dollar but since January

2003 there has generally been a stable relationship between crude oil prices and the exchange rate.

The ASEAN membership provides a sample of emerging; less developed; and developed nations, a

comparison that can lead to a better understanding of the gains or shortfalls from changes in prices of

fossil fuel which tend to dominate the energy mix of many nations. Further, we account for several time

series modelling issues—namely, endogeneity, serial correlation and heteroscedasticity—and apply

estimation methods to derive long- and short-run models that can allow for comparisons between

countries and the fossil fuels.

13 A plethora of studies have examined the economic growth and energy consumption nexus. Some of the recent studies that show support of the conservative hypothesis for developing countries include Lee and Chang (2007); Huang et al. (2008); and Ozturk, et al. (2010).

3. Some trends in production, consumption, net exports and net imports of oil, gas and coal

and real exchange rates over the period 2008-2012

Table 1 reports average production, consumption, net exports, and net imports of each of the fossil fuel

energy commodities in ASEAN member countries over the period 2008 to 2012. Total population of

each ASEAN member as well as its proportion against the ASEAN total population are provided in

Columns 2 and 3, respectively.

The ASEAN member nations consume crude oil in larger proportions than natural gas and coal.

Indonesia is ASEAN’s largest consumer of crude oil and coal, with her population close to 41% of the

ASEAN population. Thailand is the largest consumer of natural gas, averaging 7733.82 billion cubic

feet over the five-year period. Indonesia was the largest producer of crude oil, with average production

at 1034.4 thousand bpd, followed by Malaysia with 723.6 thousand bpd. 14

Overall, for the period 2008 to 2012, the net ASEAN exporters of crude oil are Brunei, Malaysia

and Vietnam. The remainder of the ASEAN members are net importers of crude oil, with Singapore

importing 165% more than the second largest importer, Indonesia.

<INSERT TABLE 1 >

In the same period (2008 to 2012) Indonesia, Malaysia and Thailand were the largest producers and

consumers of natural gas within the ASEAN group. The group was a net exporter of natural gas, with

Singapore and Thailand the exceptions, as net gas importers. Indonesia produced most of the coal of

ASEAN, producing 674.9% more than the next largest producer, Vietnam. All member countries,

except Brunei, were consumers of coal. Overall, the region has been a net exporter of coal; with

Indonesia being the largest exporter in 2013 (421.71 million short tons).

14In 2013, Brunei and Malaysia were the only two crude oil exporters in the region after Indonesia suspended its OPEC membership in 2008. Indonesia suspended its membership of OPEC in 2008 as it had become a net crude oil importer and re-joined in June 2015, see http://www.reuters.com/article/2015/06/05/us-indonesia-opec-deals-idUSKBN0OL0QM20150605.

Displayed in Table 2 is the energy mix, or the composition of the energy usage, which is vastly

different from country to country. Oil, for instance, is the major energy source in 70% of ASEAN

countries, namely, Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam.

Oil was second to gas in Brunei, and Myanmar. Gas is the second major energy source for Indonesia,

Malaysia, Singapore and Thailand. Coal is the major source of energy for Laos. Coal takes the second

position in the energy mix of Cambodia, the Philippines, and Vietnam but for 60% ASEAN countries, it

is the least consumed fossil fuel.

<INSERT Table 2>

In Table 3 we summarise the exchange rate regimes for the ASEAN members. The key message

lies with the apparent heterogeneity in the exchange rate regimes within the ASEAN member nations

and therefore the need to examine each nation’s exchange rate separately.

<INSERT TABLE 3>

4. Empirical Methods

We examine the long- and short-run relationships between RER and real energy commodity prices

(REP), being the real oil price, the real gas price, or the real coal price. We begin by establishing a

cointegration (Johansen 1991 and 1995) long-run relationship between two variables for the ASEAN

members.

For a vector Y tof non-stationary I(1) variables (LRER t , REP t), a VAR of order p takes the

following form:

Y t=A1 Y t−1+…+ A pY t− p+ βX t+εLRt (1)

where X t is a vector of deterministic variables and ε t is a vector of innovations. Equation (1) can be

rewritten as ∆ Y t=∏ Y t−1+∑i=1

p−1

τ ∆Y t−1+βX t+εt; where ∏ ¿∑i=1

p

A i−I ; and τ=− ∑j=i+1

p

A j. We test all five

cases of the deterministic trend, described in Johansen (1995) using the trace and the maximum

eigenvalue statistics.15

If a long-run cointegrating relationship is found, we examine the relationship further using the

following long-run model:

RER t=α1+β1 REP t+εLR 1t (2)

where, α 1 and β1 are parameters to be estimated and ε t is the short-term innovations;RERt is the

logarithmic form of the real exchange rate; and REP t is the log of real price of energy. To accommodate

the energy prices of oil, gas, and coal, three models are estimated using OLS with White’s (1980)

consistent covariance matrix. This long-run real exchange rate model with oil prices has also been

examined previously (see Lizardo and Mollick, 2010; Chen and Chen, 2007). These studies examine the

OECD countries while our focus is on ASEAN.

The standard short-run model takes the following form:

∆ RERt=α 1+β1∑0

z

∆ REPt−z+ β2 ∆ RER t−1+β3 εt−1+ε SR 1 t (3)

where, α 1 and β ' s are parameters to be estimated. To define the new variables: ∆ RER is the log of real

exchange rate returns;∆ REP depicts the change in the log of real fossil fuel price; ∆ RERt−1 signifies the

AR(1) structure; and ε t−1 denotes the MA(1) structure.16 Once again, three versions of this model are

estimated for each ASEAN country to accommodate the three fossil fuels. The model was estimated for

up to four lags to determine the best size of z for each fossil fuel: the best model is determined using the

usual selection criteria. For brevity we only report the contemporaneous and one lagged effect. The

15 Data trends for the five cases include: none, none, linear, linear, and quadratic and test types to coincide with these are: no intercept and trend; intercept and no trend; intercept and no trend; intercept and trend; and intercept and trend, respectively.

16 With the aim to draw comparison across our short-run models (3-7), we use a standard format ARMA framework for the short-run models.

models are examined within the GARCH framework to account for heteroscedasticity using the

Bollerslev-Wooldridge (1992) consistent covariance matrix.

As part of our robust tests, we augment Equation (3) with other important determinants of RER: real

interest rate differential ¿productivity differential (Y ¿¿¿; and a financial crisis (FC).17 The FC variable

takes the value of 1 during a financial crisis or zero otherwise. This gives Equation (4)

∆ RER t=α 1+β1∑0

z

∆ REP t−z+ β2 ∆ RER t−1+β3 εt−1+β4 R¿¿+β5 Y ¿

¿+β6 FC 1+εSR 3 t ¿¿

(4)

All other variables in Equation (4) are from the previous model. We also investigate whether the short-

run relationships are different during periods of financial crisis (FC) and non-financial crisis. For this,

we estimate the following models (5) and (6):

∆ RER t=α 1+β1(∆ REP¿¿ t × FC )+β2 ∆ RERt−1+ β3 εt−1+ β4 R¿¿+β5Y ¿

¿+ε SR 4 t ¿¿¿

(5)

∆ RER t=α 1+β1(∆ REP¿¿ t × NONFC )+β2 ∆ RERt−1+β3 εt−1+β 4 R¿¿+β5Y ¿

¿+εSR 5 t ¿¿¿

(6)

While Equation (5) tests the short-run relationship between RER returns and REP during the FC

periods, Equation (6) examines this relationship periods excluding the FC or the NONFC periods,

where NONFC=1−FC.

5. Data

17 These variables appear in first differenced form if I(1) or in levels in I(0). Our choice of the first two variables is informed by the uncovered real interest rate parity and the well-known Balassa-Samuelson model, respectively. Some empirical studies that examine exchange rates using these variables are, for example, Meese and Rogoff (1988) on the relationship between real interest rate differential and real exchange rate; Camarero and Tamarit (2002) on real interest rate differentials and oil prices; and Chen and Chen (2007) on using both real interest rate and productivity differentials.

This study employs balanced monthly data from the period 1992:1 to 2013:12. Oil price, production,

export, import and consumption statistics on the three energy commodities are provided and

amalgamated from EIA data. Exchange rates (using the US dollar expressed in terms of local currency,

where an increase in exchange rate indicates a depreciation of the local currency against the US dollar);

the CPI for each of the ASEAN members; and the prices of coal and natural gas are sourced from the

database of Emerging Market Economic Data of Emerging Asia and the International Monetary Fund’s

International Financial Statistics (IFS) on DxTime.18

Additional control variables include the real interest rate differential(R¿¿r−r¿), and the logarithm of

the productivity differential¿). Real interest rates for ASEAN members (r) series are derived by

subtracting CPI inflation rates from the nominal interest rates. The productivity measure (logarithm of

real GDP less logarithm of employment) of an ASEAN member is represented by Y. The variables with

an asterisk (*) relate to the corresponding variables for the US. Data for these two variables are sourced

from the IFS.

In standardising the data to facilitate easy comparisons across the ASEAN members we

encountered a few issues, for instance some CPI data was missing for Brunei, Cambodia, Laos,

Myanmar and Vietnam, and we filled these gaps with averages rolling over six months.

Brent crude data is currently a benchmark for two-thirds of the world’s crude oil market. Further, as

oil fields in the Asian region are considered mature, the decline in production of Tapis crude, a

Malaysian produced crude oil blend, has resulted in prices of Asia-Pacific crude being pegged to the

price of Brent crude—hence we use Brent crude data.

The Asia-Pacific benchmark for coal is Newcastle Coal, which is the Australian thermal coal

exported from the Port of Newcastle. For natural gas (liquid natural gas in Japan), Indonesian price data

is utilised as it is the price benchmark for the Asia Pacific.

18 It should be noted that during the study period, Cambodia and Laos were not involved in production or consumption of natural gas while Brunei did not produce or consume coal. We therefore exclude these from the respective analyses.

5.1 Standard descriptive statistics on the data

Presented in Table 4 are some standard statistics on the fossil fuel prices and each nation’s exchange

rate against the US dollar. The mean of the price series is different for each country because all are

expressed in real terms (deflated by the country-specific CPI) and in local currency terms. Further, the

series are in logarithmic terms.

<INSERT TABLE 4>

The coefficient of variation (CV) suggests that real oil price is the most volatile in most of the

ASEAN member countries (Cambodia, Indonesia, Laos, the Philippines, Thailand, and Vietnam). This

is consecutively followed by gas and coal prices. In the other ASEAN members (Brunei, Malaysia,

Myanmar, and Singapore) real gas price is much more volatile, followed by oil, and then coal price.

From Column 6 in Table 5 it is also clear that most series are not normal. The Jarque-Bera test of the

null when series are normal is rejected for all countries except Cambodia. The probabilities related to

the Q-statistics of the squared of the variables (Columns 7-9) and the White (1980) test results (Column

11) together point to the presence of ARCH/GARCH effects, and hence the inappropriateness of using

the OLS estimation procedure. We deal with this by using either the GARCH method with Bollerslev-

Wooldridge (1992) robust standard errors and covariances for the estimation of short-run models or the

OLS with White’s (1980) heteroskedastic covariance matrix for the long-run estimations. Further, serial

correlation in the residuals is non-existent by the fourth order of the lags for most variables other than

the RER returns (see LM test results in Column 10). In fact, the information selection criteria of the

models also confirm this later, as best fit models take either an AR(1) or ARMA(1,1) structure.

In Table 5, we present the time series properties of the variables tested using the standard ADF test.

We find that coal, gas, and oil prices in local currency are non-stationary for all countries, except

Cambodia (coal). Two additional variables, real interest rate and productivity differential with the US, to

be used as part of testing the robustness of our results, are also tested. We find that real interest rate (in

levels form) is stationary for Cambodia, Indonesia, Laos, the Philippines and Vietnam but non-

stationary or I(1) for the other countries. In levels form, the productivity differential with the US is

stationary only for Malaysia.

<INSERT TABLE 5>

6. Empirical Results

6.1 Long-run cointegration relationship

The Johansen test, under various specifications as explained above, are applied to countries with I(1)

RER returns and fossil fuel variables. Cambodian data was not put through this test as both variables

was found to be I(0).

The results summarised in Table 6 suggest heterogeneity in long-run cointegrating results across the

three fossil fuels for the ASEAN group, something that previous studies has failed to highlight. A

maximum of one cointegrating relationship in the presence of an intercept, and/or with the assumption

of a linear or quadratic trend of the data, is true for all countries except Brunei and Myanmar.

Nonetheless, this is found under a variety of settings and results are not consistent across the fossil fuels.

In the presence of an intercept and a linear trend, cointegration is suggested only for Indonesia and

Vietnam. Indonesia shows a maximum of two cointegrating relationships in the case of oil and gas,

while Vietnam only shows one in the case of oil. Laos, Malaysia, the Philippines, Singapore and

Thailand show one cointegrating relationship in the presence of a quadratic trend assumption of the data.

We pay attention to these deterministic features when estimating long-run relationships.

<INSERT TABLE 6>

6.3 Long-run relationships

We begin by conducting the endogeneity test. We test for endogeneity in the nexus between exchange

rates and energy prices as there is some evidence that exchange rate can influence oil price (see, Zhang

et al., 2008; Bloomberg and Harris, 1995 and Akram, 2009). We conduct the endogeneity test following

Narayan (2013). Column 2 of Table 7 presents a summary of the results. The reported parameters

(presented in panels 1-3 in Table 7) of models that do show endogeneity appear in bold. Endogeneity is

rejected for all, with the exception of the quadratic trend model for Thailand (gas) and Vietnam (coal) in

the long run.

Consistent with the cointegration analysis, the long-run relationships are estimated across the

three scenarios explained above. The results are consistent across the linear and quadratic trends but we

note some variations when trend is not applied. Further, the model suffers from heteroskedasticity

(confirmed by the White test and Q-stats of the squared of the variables, see Table 4). Hence we use the

consistent covariance matrix of White (1980) to account for the heteroskedasticity.

Evidence of the long-run effect of fuel fossil fuel prices is limited. For the significant results,

prices of oil, gas and coal show a positive influence, implying a depreciation of the local currency

against the US dollar after an increase in the price of these fuels. This result is consistent with studies

that cover some OECD countries (see discussion Section 2). Only in the case of Vietnam, we notice that

higher fuel prices appreciate Vietnam’s Dong against the US dollar, a result consistent with Narayan

(2013) with respect to oil.

<INSERT TABLE 7>

Across the three fossil fuels, we find that gas had the strongest influence, followed consecutively by

oil and coal. Comparison of the long-run results across ASEAN members suggests that the Indonesian

exchange rate has been most impacted—this is true across all fossil fuels. When Indonesian currency is

depreciated against the US dollar with higher fossil fuel prices the impact is less than proportional, but

strongest in the case of gas price, followed by oil and coal. Second in line is Thailand where the Baht

falls against the US dollar after both an increase in the price of oil and gas. Between these two nations,

Thailand has been the largest consumer of gas and Indonesia oil and coal, at least over the last five years

studied.

Despite being a net importer of fossil fuel, Singapore’s exchange rate with the US seems to be least

affected by price changes in the fossil fuels, making it the most resilient nation in the face of long-run

fossil fuel shocks. The long-run cointegration link for oil and gas is non-existent for Singapore and the

size effect of coal is lower than for other ASEAN members. The others (in particular, Indonesia,

Malaysia and Laos) need to mould their energy strategies and policies to reduce the adverse effects of

fossil fuel shocks.

6.4 Short-run relationship (Equation 3)

Equation 3 (the baseline model) is estimated within the GARCH framework with Bollerslev-

Wooldridge (1992) robust standard errors and covariance. In Table 8, Short-run relationships (Baseline

model), we report the results relating to the best model chosen by the Schwarz Information Criterion

(SIC), Akaike Information Criterion (AIK), and Hannan-Quinn Information Criterion (HQC) from a

series of models estimated using up to 4 lags and AR(1) and ARMA(1,1) structures.

For all fossil fuel prices, the key result suggests that higher prices lead to a depreciation of the local

currency against the US dollar, consistent with most empirical literature (Section 2). The effect is

instantaneous and in some cases we find a lagged effect of the prices. The first order auto regressive

(AR) term is also significant for most countries, signalling persistence of shocks. We find that

appreciation or reversals are also possible one month after prices increase.

<INSERT TABLE 8>

The specific results are as follows. The RER returns in Cambodia, Indonesia, Laos, Malaysia,

Myanmar, the Philippines, Thailand, and Vietnam respond positively to changes in oil prices. For gas,

all countries replicate this positive relationship. For an increase in coal price, RER returns for all, except

Brunei, Singapore, and Vietnam, are found to increase. Our results are as expected (see Section 2).

Reboredo (2012) noticed that the intensity was higher for oil exporting countries and lower for oil

importing nations. We find no such pattern between net exporters and importers, probably because the

nations examined here have experienced movement to and fro, i.e. between net exporter and net

importer positions over the study period.

Across the three fossil fuels, we notice that the contemporaneous effect of at least one fuel price is

felt by all, except Brunei, highlighting the importance of fossil fuel as a determinant of RER returns in

the short run. This also means that changes in fossil fuel prices are contributing to their price

competitiveness. The contemporaneous oil price effects are largest for Myanmar and lowest for Laos.

The Philippines and Malaysia fall in between. Unlike Brunei, no short-term effects of oil are seen for

Cambodia, Indonesia, Singapore and Thailand (Table 9).

The impact of gas price changes (i.e. depreciation of the exchange rate) is strongest for Thailand and

weakest for Singapore. Only Brunei is unaffected by gas price changes (Table 10). For coal, Myanmar

shows the most impact while Cambodia shows the least impact. Falling in between these nations in

descending order are Indonesia, Laos, and Malaysia (Table 9).

6.5 Short Run: inclusion of other determinants of real exchange rates (Equation 5)

Tables 9 and 10 capture results inclusive of the variables: R_R*_; Y_Y*_; and FC. The sign effects of

fossil fuel prices are generally consistent with the previous section, However, compared to Section 6.4,

we do note changes in the number of significant relationships and an increase in the number of delayed

effects of the prices, or persistence in the effect in the next month.

For oil, the depreciating effect of oil price is significant in the case of Indonesia and Cambodia. For

Vietnam and the Philippines, the depreciating effect of oil price increase is lower. Further, one-month

delayed effects are found for Laos and Myanmar. We also note that for Laos the price effect changes

sign (Table 9). The depreciating effects of gas prices is still overwhelming, but is no longer found for

Myanmar.

<INSERT TABLE 9>

Coal also shows short-term positive effects consistently across all nations, except for Laos and

Malaysia. Singapore, Vietnam, Indonesia and Myanmar are no longer affected by coal price changes.

Cambodia and Laos show delayed but appreciating effects of coal price a month after the increase in

coal price, with Cambodia showing results just short of full reversal (Table 10).

<INSERT TABLE 10>

Next, we compare the size effects across: (1) the three fossil fuels; and (2) the ASEAN members for

each fossil fuel. Across the three fossil fuels, the contemporaneous or delayed effect of at least one fuel

price is felt by all ASEAN countries (Tables 9 and 10). Of the three fossil fuels, the influence of gas is

strongest for most countries. This is consecutively followed by coal and oil. For Cambodia and

Myanmar however, the influence of coal price is stronger than that of oil or gas.

Focusing only on significant contemporaneous effects across ASEAN, we can say that oil price

effects are strongest for the Philippines as nearly all its oil consumption is met by imports (see Table 1).

The oil effect is weakest for Cambodia for which oil consumption is at the lower end within the ASEAN

group, while in terms of Cambodia’s energy mix, oil makes up more than 95% of Cambodia’s fuel

consumption. We notice a change in ranking from Table 8. Indonesia and Vietnam fall in between.

Myanmar shows a stronger one-month delayed effect than Laos. The others show no short-term effects

for oil.

The immediate impact of gas is strongest for Singapore (a net importer) and weakest for Brunei (a

net importer) and Thailand (a net exporter). Again, we note a sharp change in ranking from the baseline

model. Only Myanmar, a net exporter and heavily reliant on gas for energy, is unaffected by gas price

changes (Table 9). For coal, Cambodia now shows the most impact as all its consumption of coal in met

by imports. On the other end is Laos (net exporter) showing the least impact (i.e. a different ranking

from Table 8). Falling in between are Myanmar, the Philippines Laos, and Indonesia in descending

order.

It is important to consider two new variables—interest rate and productivity differential—when

examining the link between fossil fuel prices and real exchange rate returns between the ASEAN group

and the US. Indeed, we say this because we find disparity in results in terms of magnitude (as shown

above through changes in rankings) and significance. In contrast, Chen and Chen (2007), found no

significant difference in results after adding real interest rates and productivity differentials in the real

exchange rate models of G7 vis-á-vis the US. It seems that the real difference in interest rate and

productivity between the ASEAN group and the US is making a difference here.

Further, the sign effects of Y_Y*_ and R_R*_ are rather consistent across countries and fossil fuels,

although alignment with theory is mostly missing. In contrast to theory, we find that higher R_R*_ leads

to a depreciation of the local currency against the US dollar.19 Similarly, a rise in productivity

differentials or Y_Y*_ according to the Balassa-Samuelson model should boost the domestic currency,

but we find that the reverse effect is also possible.

For the oil price related models, the depreciating effect of R_R*_ is significant and consistent

across most ASEAN countries, namely Indonesia, Laos, Myanmar, the Philippines, and Singapore.

Higher Y_Y*_ leads to an appreciation of the local currency of Myanmar and Singapore but

depreciation in the case of Indonesia.

19 See Meese and Rogoff (1998).

Models relating to gas price show the depreciating effects of interest rate differentials for Malaysia,

the Philippines, Singapore, and Vietnam. The appreciating effects of productivity differentials are noted

for Myanmar.

For coal price related models, higher R_ R*_ depreciates the local currency against the US dollar

for Indonesia, Laos, the Philippines, and Singapore. Higher Y_Y*_ leads to an appreciation of the local

currencies of the same countries as in the case of oil price models, i.e. Myanmar, Singapore, and

Vietnam. Depreciation of local currency against the US dollar is found for Indonesia and Thailand.

The FC variable captures the effects of the financial crises, namely the Asian financial crisis of

1997:07 to 1998:12, the bursting of the dot.com bubble of 2001, and the global financial crisis of

2007:02 to 2009:12.20 Significant FC effects on RER returns are found for four ASEAN members only:

Cambodia, Indonesia, and Laos (across all models with the three fossil fuels); and Myanmar (for models

relating to prices of oil and gas). The effect across models is consistently positive, suggesting that FC

led to real depreciation of the local currency against the US dollar. The only exception is Indonesia (oil),

where the converse is true.

6.6 During Financial crisis (FC) and non-FC periods

In the previous section, while we noted that a financial crisis affects RER returns, we were unable to say

anything about whether there is a difference in the effects of energy prices on RER returns during a

financial crisis and during periods without such a crisis. To examine this asymmetry in the effect of

energy prices on the RER returns, we estimate Equations (6) and (7). Table 11 shows the relationship

between energy prices and RER during the three financial crises and financial crisis-periods.

The key findings are as follows. An increase in the cases of significance from 40% (Section 6.5) to

90% of the ASEAN members. All but Singapore’s RER returns are found to respond to FC variables

here. Again we note the reliance of the Singaporean economy on the effects of energy prices despite

20 We tested changing dates by three to five months and results remained unaffected.

heavy dependence on imports. Columns 7-10 in Table 11 indicate the periods that show cases of weaker

results (W). We define weaker results as those which are insignificant and/or smaller in size between the

FC and non-FC periods.

In all, we find 18 out of 30 cases (oil, six; gas, four; and coal, eight) where the effects of a fossil

fuel prices on RER are found to be weaker during FC periods than non-FC periods. This is most

prevalent in the case of coal, followed by oil.

Conversely, 12 out of 30 cases (oil, four; gas, four; and coal, two) show that the influence of fossil

fuel prices is stronger during FC periods. This is most prevalent in the case of gas and oil and least in

the case of coal.

There are three important implications of these results. First, after taking a tally of the lowest losses

or largest gains for each fossil fuel across countries, our results suggest that oil and coal were the most

competitive during the FC period and during the non-FC period, oil alone was the most competitive.

Gas was found to be the least competitive between the two periods.

Second, a majority of cases reinforce the positive link between economic activity and energy usage

as we find that the link between fossil fuel prices and exchange rate is stronger in generally good times

(or non-FC periods) and weaker in extremely bad times (FC periods).

Our results reveal an asymmetric response of real exchange rate to real fossil fuel price during FC

and non-FC periods. Khalifa et al. (2015) show the presence of asymmetry in the energy (oil and gas)

and four OECD currency series interactions during expansionary and contractionary phases of the

business cycle. The study finds that this asymmetry is stronger in oil than in natural gas. However, our

results show that for ASEAN, if we only consider financial crises, then the asymmetry is stronger for

gas than oil or coal (Table 11, Columns 3-5).

Reboredo (2012) and Reboredo and Rivera-Castro (2013), show a strong dependence between oil

prices and US exchange rates during the GFC period but a weak relationship in the pre-crisis period,

suggesting reduced gains from diversification in these two markets during the financial crisis. We show

that the effects of oil and coal prices on RER are found to be weaker during FC periods than non-FC

periods. However, exchange rate responses to gas prices are stronger during the FC periods.

<INSERT TABLE 11>

7. Summary and policy implications

This paper compared the influence of prices of three fossil fuels on real exchange rates in both the long-

and short-run. Our econometric processes paid attention to the presence of heteroscedasticity and serial

correlation when estimating the real exchange rate models for ASEAN members. We also account for

other determinants of real exchange rate and asymmetry in the link between fossil fuel prices and real

exchange rate returns due to financial crises.

In doing so, we provided new and comprehensive insights into the ASEAN members’ relationships

between real exchange rates and fossil fuel prices. We discovered that higher prices of these three fossil

fuels (oil, gas and coal) have contributed to depreciation of ASEAN currencies against the US dollar.

We found difference in intensity in the impact of energy prices across the fossil fuels and across nations.

We explained almost all these differences by examining the external exposure of the domestic energy

markets, the energy mix of the nations, and/or the dependence of a nation on imports for energy needs.

From the results, it is clear that changes in fossil fuel prices are factored in the US dollar-based real

(spot) exchange rate returns for most ASEAN countries, implying that traders need to hedge against

variations in exchange rate and changes in fossil fuel prices to minimise their losses. Further, while the

competitive nature of coal pricing is readily known, our paper brings to light the ability of coal to exert

least pressure/risk (relative to oil and natural gas) on the ASEAN members. This finding lends some

support to forecasts from international agencies on the exponential growth of coal demand among

ASEAN members. This finding also supports the ASEAN initiatives to continue to enhance cooperation

and increase investment in clean coal technology for a greener future.

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Table 1: Population and average production, consumption, net exports and imports for the period 2008-2012

(1)Population~

(million)

(2)Population~

(% of ASEAN population)

(3)Production*

(4)Consumption

(5)Net Exports

(6)Net Imports

Crude Oil (Thousand Barrels per Day)

Brunei 0.412 0.07 159.072 16.862 142.21

Cambodia 14.865 2.44 0.136 33.844 - 33.708

Indonesia 246.864 40.58 1034.436 1482.716 - 448.278

Laos 6.646 1.09 - 3.192 - 3.192

Malaysia 29.24 4.81 723.634 617.518 106.116 -

Myanmar 52.797 8.68 20.73 27.344  - 6.616

Philippines 96.707 15.9 30.37 304.574  - 274.206

Singapore 5.312 0.87 20.926 1208.238  - 1187.312

Thailand 66.785 10.98 435.994 948.446  - 512.452

Vietnam 88.775 14.59 333.758 332.002 1.756 -

Natural Gas (Billion Cubic Feet)

Brunei 0.412 0.07 430.854 110.902 319.952 -

Cambodia 14.865 2.44  -  -  - -

Indonesia 246.864 40.58 2624.488 1317.056 1307.432  -

Laos 6.646 1.09  -  -  - -

Malaysia 29.24 4.81 2162.142 1112.124 1050.018  -

Myanmar 52.797 8.68 421.43 119.922 301.508  -

Philippines 96.707 15.9 103.868 103.868 -  -

Singapore 5.312 0.87  - 306.606  - 306.606

Thailand 66.785 10.98 1230.312 1546.764  - 316.452

Vietnam 88.775 14.59 271.102 271.102  - -

Coal (Million Short Tons)

Brunei 0.412 0.07  -  -  -  -

Cambodia 14.865 2.44  - 36.376  - 36.376

Indonesia 246.864 40.58 367765.68 63003.046 304762.64  -

Laos 6.646 1.09 999.656 578.716 420.942  -

Malaysia 29.24 4.81 2593.96 23089.432  - 20495.474

Myanmar 52.797 8.68 847.898 847.898  -  -

Philippines 96.707 15.9 6575.728 14773.398  - 8197.67

Singapore 5.312 0.87  - 5.512  - 5.512

Thailand 66.785 10.98 20738.772 39433.092  - 18694.318

Vietnam 88.775 14.59 47460.898 26888.24 20572.658  -

Sources: Energy Information Administration and authors’ calculations, http://aseanup.com/asean-infographics-population-market-economy/Notes;* Crude oil production (includes lease condensates), natural gas plant liquids, and other liquids, and refinery processing gain.`indicates that the country is not a producer of the energy source. ~Total population data is as of 2012. Grey shading indicates countries that did not consume and produce the fossil fuel during the study period. Accordingly, we exclude these from our estimations.

Table 2: Fossil fuel mix for the period 2008-2012

Oil Gas Coal Total Oil % Gas % Coal %

Brunei 0.174 0.596 0.000 0.770 22.592 77.408 0.000

Cambodia 0.347 0.000 0.004 0.351 98.798 0.000 1.202

Indonesia 15.239 7.178 7.322 29.740 51.242 24.136 24.622

Laos 0.032 0.000 0.059 0.092 35.463 0.000 64.537

Malaysia 6.300 5.855 2.339 14.494 43.467 40.397 16.136

Myanmar 0.282 0.632 0.094 1.007 27.976 62.733 9.291

Philippines 3.132 0.534 1.539 5.205 60.159 10.266 29.574

Singapore 12.881 1.564 0.001 14.445 89.170 10.825 0.005

Thailand 9.667 7.556 3.374 20.597 46.933 36.684 16.382

Vietnam 3.402 1.404 2.838 7.644 44.503 18.372 37.125Sources: Energy Information Administration and authors’ calculations.Note: Oil, gas, and coal consumption figures are presented in British thermal units (btu).

Table 3: ASEAN exchange rate regimes 1992-2013

Sample Period Exchange Rate Regime

Brunei 1992:1 - 2013:12 Currency Board with other exchange rate anchors.

Cambodia 1992:1 - 2013:12 Stabilised Exchange Rate Arrangement with USD anchor.

Indonesia 1992:1 - 2013:12 Crawl-like exchange rate arrangement with inflation targeting framework.

Laos 1992:1 - 2013:12 Stabilised Exchange Rate Arrangement with other exchange rate anchors.

Malaysia 1992:1 - 2013:12 Managed Arrangement.

Myanmar 1992:1 - 2013:12 Managed Arrangement.

Philippines

1992:1 - 2013:12 Floating exchange rate arrangement with inflation targeting framework.

Singapore 1992:1 - 2013:12 Crawl-like exchange rate arrangement with composite exchange rate anchors.

Thailand 1992:1 - 2013:12 Floating exchange rate arrangement with inflation targeting framework.

Vietnam 1992:1 - 2013:12 Stabilised exchange rate arrangement with composite exchange rate anchors.

Source: IMF, 2013, Annual Report on Exchange Arrangements and Exchange Restrictions.Note: Includes countries that have no explicitly stated nominal anchor but monitor various indicators in conducting monetary policy.

Table 4: Standard Descriptive Statistics for Real Fossil Fuel Prices (in logs) and Exchange Rate (RER, in logs).

CountriesSeries

(in logs) MeanStd. Dev.

CV(%)

J-B (prob.

)

Q-stat prob.

(2 lags)

Q-stat prob.

(4 lags)

Q-stat prob.

(10 lags)LM test (4, 255)

White test

Prob. F(2, 258)

BruneiCOALGAS 2.270 0.457 20.112 0.002 0.000 0.000 0.000 0.942 0.000OIL 4.078 0.631 15.481 0.000 0.000 0.000 0.000 0.603 0.000RER 0.308 0.115 37.459 0.001 0.000 0.000 0.000 0.000 0.318

CambodiaCOAL 12.360 0.341 2.762 0.054 0.000 0.000 0.000 0.003 0.000GASOIL 7.593 0.264 3.475 0.561 0.000 0.000 0.000 0.000 0.221RER 8.597 0.015 0.178 0.226 0.000 0.000 0.000 0.185 0.000

IIndonesia

COAL 13.358 0.382 2.859 0.001 0.000 0.000 0.000 0.468 0.000GAS 11.261 0.421 3.740 0.004 0.000 0.000 0.000 0.635 0.000OIL 8.463 0.576 6.806 0.000 0.000 0.000 0.000 0.932 0.000RER 9.299 0.271 2.909 0.000 0.000 0.000 0.000 0.015 0.001

LaosCOAL 13.316 0.298 2.238 0.000 0.000 0.000 0.000 0.810 0.000GASOIL 8.420 0.500 5.933 0.000 0.000 0.000 0.000 0.172 0.000RER 9.256 0.228 2.461 0.002 0.000 0.000 0.000 0.000 0.001

MalaysiaCOAL 5.246 0.427 8.145 0.000 0.000 0.000 0.000 0.563 0.001GAS 3.148 0.462 14.665 0.001 0.000 0.000 0.000 0.786 0.000OIL 4.955 0.636 12.838 0.000 0.000 0.000 0.000 0.565 0.000RER 1.186 0.155 13.047 0.000 0.000 0.000 0.000 0.004 0.012

MyanmarCOAL 7.456 1.410 18.912 0.000 0.000 0.000 0.000 0.996 0.581GAS 5.357 1.483 27.686 0.000 0.000 0.000 0.000 1.000 0.254OIL 7.164 1.411 19.693 0.000 0.000 0.000 0.000 0.990 0.787RER 3.395 1.427 42.039 0.000 0.000 0.000 0.000 0.987 0.284

PhilippinesCOAL 7.944 0.406 5.109 0.000 0.000 0.000 0.000 0.638 0.001GAS 5.846 0.445 7.618 0.000 0.000 0.000 0.000 0.886 0.000OIL 7.652 0.620 8.100 0.000 0.000 0.000 0.000 0.557 0.000RER 3.884 0.185 4.765 0.000 0.000 0.000 0.000 0.000 0.129

SingaporeCOAL 4.427 0.388 8.775 0.000 0.000 0.000 0.000 0.579 0.002GAS 2.329 0.408 17.521 0.002 0.000 0.000 0.000 0.975 0.000OIL 4.136 0.587 14.195 0.000 0.000 0.000 0.000 0.656 0.000RER 0.367 0.130 35.435 0.000 0.000 0.000 0.000 0.002 0.017

ThailandCOAL 7.613 0.385 5.054 0.000 0.000 0.000 0.000 0.901 0.000GAS 5.514 0.426 7.734 0.002 0.000 0.000 0.000 0.952 0.000OIL 7.321 0.597 8.161 0.000 0.000 0.000 0.000 0.601 0.000LRER 3.553 0.172 4.848 0.000 0.000 0.000 0.000 0.000 0.000

VietnamCOAL 13.988 0.365 2.610 0.000 0.000 0.000 0.000 0.493 0.000GAS 11.889 0.392 3.296 0.003 0.000 0.000 0.000 0.431 0.000OIL 13.696 0.567 4.140 0.000 0.000 0.000 0.000 0.727 0.000LRER 9.928 0.139 1.398 0.000 0.000 0.000 0.000 0.652 0.450

Note: All variables are expressed in local currency.

Table 5: Unit Root Test

This table presents the ADF test results for the levels of the three fossil fuel price variables (Coal, Gas, Oil) and real exchange rate (RER) as well as two additional variables real interest rate (R_R*_) and productivity (Y_Y*_) differentials. All variables appear in log form exceptR_R*. * denotes level of significance at 5 per cent or better.

Coal Gas RER Oil R_R*_ Y_Y*_

Brunei 0.816 0.605 0.761 0.146 0.026*

Cambodia 0.022* 0.000* 0.072 0.002* 0.736

Indonesia 0.192 0.628 0.132 0.671 0.036 NA

Laos 0.111 0.641 0.376 0.004* NA

Malaysia 0.573 0.769 0.435 0.728 0.129 0.000*

Myanmar 0.751 0.847 0.700 0.854 0.060 0.327

Philippines 0.454 0.676 0.644 0.661 0.002* 0.939

Singapore 0.470 0.716 0.868 0.766 0.093 0.573

Thailand 0.391 0.652 0.387 0.654 0.206 0.151

Vietnam 0.382 0.641 0.978 0.605 0.007* 0.773

Table 6: Johansen co-integration test results: RER returns and prices of oil, gas, and coal

This table summarises the Johansen co-integration results based on the trace and the Maximum Eigen values

derived from: the trace statistic: LRtr (r|k )=−T ∑i=r+1

k

log (1−γi¿),¿where γi is the ith largest eigenvalue

of the П matrix; and the maximum eigenvalue statistic: LRmax (r|r+1 )=−T log ( 1−γ r+1 ) . Up to 4 lags were applied. The co-integration tests were carried out with and without intercept and trends. Columns 7 and 8 display the assumption made regarding the data trend type; here N/A means co-integration with no trend; Yes indicates rejection of the null hypothesis and the acceptance of the alternative which is the presence of the long run co-integration; while None indicates the opposite. `Cambodia was not included in the co-integration tests as most variables were I(0) at all levels. The detailed results of the test results are available upon request.

Max. co-integrating relationship Co-integration Data Trend

Oil Gas Coal Linear Quadratic

Brunei 0 None None None

Cambodia` 0 - - - - -

Indonesia 2 Yes Yes Yes * *

Laos 1 Yes - None - *

Malaysia 1 Yes Yes None - *

Myanmar 1 None None None N/A N/A

Philippines 1 None - Yes - *

Singapore 1 None None Yes - *

Thailand 1 Yes Yes None - *

Vietnam 1 Yes - Yes * *

Table 7: Long-Run Results: RER and prices of oil, gas, and coal

Columns 3-11 present the estimated parameters of the long-run model (2): RER t=α1+β1 REP t+εLR 1t , where, α 1 and β1 are parameters to be estimated

and ε t is the long-term innovations;RERt is the real exchange rate; and REPt represents real price of one of the three fossil fuels. All variables appear is logarithmic form. To accommodate the energy prices of oil, gas, and coal, three models are estimated using OLS with White’s (1980) consistent covariance matrix. Column 2 of the table summarises the endogeneity tests for each country. Following Narayan (2013), the testing procedure checks whether the effects of ε LR 1t , derived from model (2), on REPt are significant at the five percent or better. Significance of this effect suggests the presence of endogeneity. This is tested for all three fossil fuels. * denotes level of significance at 5 per cent or better. The values in bold indicate that the energy price (or index) was detected to be endogenous.

Panel 1: Intercept, No trendPanel 2: Intercept, Linear trend Panel 3: Intercept, Quadratic trend

Endogeneity OIL GAS

COAL OIL GAS

COAL OIL GAS

COAL

Cambodia - - - - - - - - - -

IndonesiaNone

- - - 0.456* 0.668* 0.225* 0.3176* 0.6086*0.2799

*

0.000 0.000 0.000 0.000 0.000 0.000

Laos None - - - - - - 0.0465* -

0.000

Malaysia None - - - - - - 0.0149 0.1138* -

0.499 0.000

Myanmar None - - - - - - - - -

Philippines

None- - - - - - - -

0.0757*

0.000

SingaporeNone

- - - - - - - -0.0011

*

0.000

Thailand Yes - - - - - - 0.0775* 0.2346* -

0.000 0.000

Vietnam

Yes

-0.019 -0.133* 0.0587* -0.189* 0.0729*

-0.0516

*

0.207 0.000 0.000 0.000 0.000 0.000

Table 8: Short-run relationships (Baseline model)

This table relates to model (3):∆ RER t=α 1+β1∑0

z

∆ REPt−z+ β2 ∆ RER t−1+β3 εt−1+ε SR 2 t, where, α 1 and β ' s are parameters to be estimated. ∆ RER is the real

exchange rate returns;∆ REP is the energy price; ∆ RER t−1 signifies the AR(1) structure; and ε t−1 denotes the MA(1) structure. * denotes level of significance at 5 per cent or better. All variables appear is logarithmic form. The results are estimated within the GARCH framework with Bollerslev-Wooldridge robust standard errors & covariance.

Oil Gas Coal

DLOIL DLOIL(-1) AR(1) MA(1) R^2 DLGAS DLGAS(-1) AR(1) MA(1) R^2 DLCOAL

DLCOAL(-1)

AR(1)

MA(1)

R^2Brunei 0.009 -0.006 0.325* 0.07 0.026 0.013 0.306* 0.10

0.426 0.560 0.000 0.071 0.315 0.000Cambodia 0.005 0.009 1.000* 0.95 0.044* -0.030 0.997* 0.96

0.605 0.319 0.000 0.010 0.055 0.000Indonesia 0.024 -0.003 0.303* 0.05 0.033* -0.014 0.259* 0.08 0.063* -0.015 0.210* 0.14

0.079 0.829 0.009 0.005 0.124 0.000 0.001 0.496 0.008Laos 0.034* -0.008 0.267* 0.12 0.060* -0.051* 0.358* 0.17

0.003 0.537 0.003 0.002 0.016 0.000Malaysia 0.036* 0.018 0.159 0.06 0.038* -0.001 0.183* 0.10 0.028* -0.011 0.169 0.06

0.009 0.071 0.064 0.019 0.896 0.034 0.049 0.366 0.082Myanmar 0.981* 0.006 0.156* 0.93 0.429* -0.086* 0.776* -0.599* 0.66 0.472* 0.036 0.953* - 0.60

0.000 0.612 0.012 0.000 0.000 0.000 0.000 0.000 0.229 0.000 0.000Philippine 0.046* 0.027* 0.330* 0.17 0.043 0.024 0.364* 0.17

0.002 0.005 0.000 0.098 0.161 0.000Singapore 0.012 -0.009 -0.471* 0.711* 0.07 0.031* 0.000 0.564* -0.413 0.06 0.014 -0.007 - 0.688* 0.06

0.248 0.356 0.001 0.000 0.032 0.984 0.006 0.066 0.391 0.657 0.003 0.000Thailand 0.048 -0.008 -0.019 0.540* 0.10 0.065* 0.043* 0.430* -0.054 0.20 0.038 -0.030* 0.471* -0.014 0.14

0.051 0.433 0.914 0.000 0.022 0.007 0.019 0.773 0.148 0.027 0.005 0.938Vietnam 0.029* 0.007 0.208 0.00 0.023 0.005 0.892* - 0.03

0.005 0.402 0.094 0.237 0.688 0.000 0.000

Table 9: Short-run relationships (Augmented Model): oil and gas

This table relates to model (4): ∆ LRER t=α 1+β1∑0

z

∆ REPt−z+ β2 ∆ RERt−1+β3 εt−1+β4 R¿¿+β5 Y ¿

¿+β6 FC 1+εSR 3 t ¿¿, where, α 1 and β ' s are parameters to be

estimated. ∆ RER is the real exchange rate returns;∆ REP is the energy price; ∆ LRER t−1 signifies the AR(1) structure; and ε t−1 denotes the MA(1) structure; FC is financial crisis variable; R_R*_ is the real interest rate differential variable; Y_Y*_ is the productivity differential variable. All variables, except R_R*_, appear is logarithmic form. * denotes level of significance at 5 per cent or better. The results are estimated within the GARCH framework with Bollerslev-Wooldridge robust standard errors & covariance.

Oil Gas

 EGARCH OILOIL(-

1) R_R*_ Y_Y*_ FCAR(1

) MA(1) R^2 GASGAS(

-1) R_R*_ Y_Y*_ FCAR(1

) MA(1) R^2

Brunei 0.000 0.002 0.001 0.038 -0.001 0.043 0.038* 0.021 0.001 0.018 -0.001 0.181* 0.090

0.979 0.890 0.665 0.275 0.629 0.022 0.180 0.653 0.581 0.706 0.019

Cambodia 0.007* -0.008 0.002* 0.130* 0.025* 0.984* 0.718

0.019 0.120 0.001 0.000 0.000 0.000

Indonesia 0.035* 0.014 0.0002 0.007* -0.032 0.326 0.075  0.055* 0.018 0.0002 -0.003 -0.316 0.512* 0.129

0.000 0.093 0.187 0.027 -0.217  2.472  0.000 0.226 0.416 0.483 0.136 0.003

Laos 0.001

-0.015

* 0.001* 0.005* 0.036

0.950 0.015 0.000 0.000

Malaysia -0.007 -0.025 0.002 0.016 0.002 -0.300 0.629* 0.100 0.050* 0.033 0.004* 0.034 -0.004 0.298* 0.750* 0.100

0.719 0.286 0.336 0.852 0.617 0.229 0.010 0.030 0.068 0.027 0.526 0.075 0.039 0.000

Myanmar -0.0060.048

* 0.003* -0.508* 0.011* 0.402* 0.521 0.072 -0.012 0.002* -0.506* 0.012* 0.455* 0.557

0.796 0.005 0.000 0.000 0.003 0.000 0.072 0.709 0.000 0.000 0.004 0.000

Philippines 0.047*0.027

* 0.001* -0.031 -0.001 0.340* 0.184

0.002 0.005 0.023 0.239 0.825 0.000

Singapore 0.006 -0.003 0.004* -0.138* 0.000 0.319* 0.801* 0.284 0.058* 0.020 0.006* -0.017 -0.003 -0.166 0.603* 0.313

0.661 0.818 0.000 0.025 0.989 0.002 0.000 0.000 0.076 0.000 0.763 0.097 0.187 0.000

Thailand 0.003 0.004 0.002 0.075 0.002 -0.069 0.464* 0.161 0.038* 0.025 0.002 0.060 0.002 0.112 0.329 0.184

0.816 0.691 0.162 0.040 0.661 0.784 0.043 0.026 0.044 0.147 0.098 0.466 0.577 0.080

Vietnam 0.017* 0.012 0.000 -0.115 0.004* -0.513 0.629 0.018

0.021 0.135 0.053 0.098 0.045 0.223 0.069

Table 10: Short-run relationships (Augmented Model): coal

This table relates to model (4):

∆ RER t=α 1+β1∑0

z

∆ REPt−z+ β2 ∆ LRER t−1+β3 εt−1+β4 R¿¿+β5 Y ¿

¿+β6 FC 1+εSR 3 t ¿¿

, where, α 1 and β ' s are parameters to be estimated. ∆ RER is the real exchange rate

returns;∆ REP is the energy price; ∆ RERt−1 signifies the AR(1) structure; and ε t−1 denotes the MA(1) structure; FC is financial crisis variable; R_R*_ is the real interest rate differential variable; Y_Y*_ is the productivity differential variable. All variables, except R_R*_, appear is logarithmic form. * denotes level of significance at 5 per cent or better. The results are estimated within the GARCH framework with Bollerslev-Wooldridge robust standard errors & covariance.

Coal

EGARCH COALCOAL

(-1) R_R*_ Y_Y*_ FCAR(1

)MA(1

) R^2Brunei

Cambodia 0.161* -0.153* 0.000 0.108* 0.034* 0.825* 0.763

0.000 0.000 0.733 0.000 0.000 0.000Indonesia 0.079* -0.0196 0.0004* -0.009 -0.128 0.392*  0.159

0.000 0.267 0.001 0.102 0.459 0.016Laos 0.023 -0.040* 0.001* 0.006* 0.068

0.057 0.005 0.000 0.000Malaysia -0.025 -0.019 0.002 0.018 0.002 -0.279 0.552 0.094

0.096 0.182 0.333 0.806 0.523 0.422 0.119Myanmar 0.266* 0.014 0.001 -0.539* 0.000 0.404* 0.697

0.000 0.779 0.120 0.000 0.978 0.000Philippine

s 0.053* 0.029 0.002* -0.023 -0.001 0.375* 0.197

0.022 0.118 0.006 0.412 0.900 0.000

Singapore -0.004 -0.001 0.004* -0.142* 0.000-

0.330* 0.810* 0.289

0.729 0.960 0.000 0.025 0.939 0.001 0.000Thailand 0.035* -0.008 0.002 0.072* 0.002 -0.040 0.472* 0.185

0.034 0.449 0.055 0.032 0.604 0.854 0.014Vietnam 0.016 0.003 0.000 -0.170* -0.006* 0.113 0.102 -0.022

0.177 0.845 0.152 0.040 0.010 0.804 0.791

37

Table 11: Short-run relationships during financial crisis (FC) and non-FC periods: oil, gas, and coal

This table relates to models (5) and (6): ∆ RER t=α 1+β1(∆ REP¿¿ t × FC )+β2 ∆ RERt−1+ β3 εt −1+ β4 R ¿

¿+β5Y ¿¿+β6 FC1+εSR 4 t ¿¿¿ and

∆ RERt=α 1+β1(∆ REP¿¿ t × NONFC )+β2 ∆ RER t−1+β3 εt −1+β 4 R¿¿+β5Y ¿

¿+ β6 FC1+ε SR 5 t ¿¿¿ . Here, α 1 and β ' s are parameters to be estimated. ∆ RER is the real exchange rate returns;∆ REP is the energy price; ∆ RER t−1 signifies the AR(1) structure; and ε t−1 denotes the MA(1) structure; FC is financial crisis variable; R_R*_ is the real interest rate differential variable; Y_Y*_ is the productivity differential variable. All variables, except R_R*_, appear is logarithmic form. Equation (5) examines the short-run relationship between RER and REP during the FC period while Equation (6) examines this relationship during non-FC periods, where NONFC=1−FC. * denotes significance at 5 per cent level or better. The values in italics are the p-values. The results are based on Bollerslev-Wooldridge robust standard errors & covariance. Columns 3 to 6 report the results estimated using the GARCH model. `Columns 7 to 10 indicate the weaker result (denoted as W) between the two periods: FC and non-FC. Weaker results are insignificant and/or relatively smaller in size.

Country Sample Oil (O) Gas (G) Coal (C) O` G` C`

Brunei FC 0.006 0.024 W W W

0.810 0.297

non-FC 0.008 0.059*

0.617 0.019

Cambodia FC 0.018* 0.059* W

0.016 0.036

non-FC 0.012* 0.159* W W

0.016 0.000

Indonesia FC -0.080* -0.056 0.062* W

0.044 0.645 0.023

non-FC -0.023 0.015* -0.004 W W

0.082 0.000 0.722

Laos FC 0.018* 0.008 W W W

0.039 0.637

non-FC 0.053* 0.113*

0.001 0.000

Malaysia FC -0.013 -0.037 -0.034* W W

0.523 0.111 0.023

non-FC 0.017 0.125* -0.007 W

0.698 0.000 0.828

Myanmar FC -0.007 0.098* -0.020 W W

0.888 0.018 0.671

non-FC -0.008 0.061 0.264* W

0.780 0.129 0.000

Philippines FC 0.057 0.033 W W

0.191 0.267

non-FC 0.051* 0.100*

0.000 0.000

Singapore FC 0.005 0.030 -0.005 W W

0.782 0.082 0.769

non-FC 0.018 0.025 -0.011 W

0.166 0.239 0.521

Thailand FC -0.046* 0.042 0.014 W

0.001 0.158 0.522

non-FC 0.029* 0.041 0.074* W W

0.003 0.075 0.001

Vietnam FC 0.065* 0.015 W

38

0.002 0.537

non-FC 0.022 0.024 W

0.099 0.131

39