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The long-run effects of geopolitical
risk on foreign exchange markets:
evidence from some
ASEAN countries
Hon Chung Hui
University of London Programmes, HELP University, Kuala Lumpur, Malaysia
Abstract
Purpose – The purpose of this paper is to analyse the long-run relationship between geopolitical risk and exchange rates in four ASEAN countries. Design/methodology/approach – We augment theoretical nominal exchange rate models available in the literature with the geopolitical risk index developed by Caldara and Iacoviello (2019), and then estimate these models using the ARDL approach to Cointegration. Findings – Our analysis uncovers evidence of Cointegration in the exchange rate models when the MYR-USD, IDR-USD, THB-USD and PHP-USD exchange rates are used as dependent variable. Next, geopolitical risk is a significant long-run driver for these exchange rates. Third, in all countries higher geopolitical risk leads to a depreciation of domestic currency. Research limitations/implications – There are implications for entrepreneurs, central banks, portfolio managers and arbitrageurs who actively trade in financial markets. Financial market players can benefit from a better understanding of how geopolitical events affect the portfolio of financial assets across various countries, while entrepreneurs can work out hedging strategies. Originality/value – This is a contribution to the study of interlinkages between political risk and foreign exchange markets. It is the first study to adopt the geopolitical risk index of Caldara and Iacoviello (2019) to the study the foreign exchange markets of ASEAN countries.
Keywords Geopolitical risk, Exchange rate, ASEAN, Cointegration Paper type Research paper
1. Introduction
The ASEAN region [1] has been noted for being a hotbed for geopolitical tensions due to its
role as a major platform on which a large part of the Cold War was fought as well as the pivot
point for international politics in the wider East Asian and Pacific region (Kitchen, 2012). Its
approach to engagement that is non-interventionist and cooperative provides a calming effect
on the region, hence the frequency in which the regional movement is being sought to
counterbalance the geopolitical ambitions of bigger global powers (Egberink and Putten,
2010). The strategic position occupied by the region is also crucial, given that a large volume
of global trade passes through the Straits of Malacca.
Historically, member countries of ASEAN too have their own domestic conflicts to grapple
with. They have witnessed proxy wars (e. the Confrontation between Indonesia and
Malaysia in the 1960s), the East Timor political crisis in 1999, the squabbles between
neighbouring countries [2], trade and territorial disputes [3] and acts of terrorism, of which the
infamous Bali bombing in 2002 is one example. Moreover, many ASEAN countries are laying
claims on the maritime zones in South China Sea particularly the Spratly Islands,
Scarborough Shoal and the Paracel Islands thus making the region a potential flashpoint for
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JEL Classification — C13, F21, F The author would like to thank the three anonymous referees for helpful comments. Gratitude is also extended to HELP University for financing this research. Any mistakes remain the sole responsibility of the author.
The current issue and full text archive of this journal is available on Emerald Insight at: emerald/insight/1746-8809.htm
Received 28 August 2020 Revised 12 November 2020 4 December 2020 Accepted 30 December 2020
International Journal of Emerging Markets Vol. 17 No. 6, 2022 pp. 1543- © Emerald Publishing Limited 1746- DOI 10/IJOEM-08-2020-
geopolitical conflicts. An on-going concern is how these types of geopolitical uncertainties
affect economic activities, especially asset prices (Bouraoui and Hammami, 2017). In the light
of such uncertainties in the ASEAN region, studies of how geopolitical risk influences
economic activities are highly relevant and timely. In this paper, we focus on a specific aspect
of the economy, namely the foreign exchange markets.
There has been a number of studies that are related to ours, of which Bouraoui and
Hammami (2017), Suleman (2017), Bahmani-Oskooee et al. (2019) and Abid and Rault (2020)
are some recent examples. Many of these studies use political risk indicators developed by the
International Country Risk Guide (ICRG) Database or develop their own customised political
indicators. In contrast, we focus more on a specific type of political uncertainty, namely
geopolitical risk as measured by the Geopolitical Risk (GPR) index developed by Caldara and
Iacoviello (2019).
Specifically, we analyse the relationship between geopolitical risk and exchange rates in a
number of ASEAN countries for which reliable data are available. These countries include
Indonesia, Malaysia, Thailand and the Philippines. We augment conventional exchange rate
models with the GPR index and then estimate these models using the bounds testing
procedure of Pesaran, Shin and Smith in an Autoregressive Distributed Lag (ARDL)
framework.
The results of our analysis are as follows. We find evidence of cointegration in the
exchange rate models when the MYR-USD, IDR-USD, THB-USD and PHP-USD exchange
rates are used as dependent variable. Next, geopolitical risk is a significant long-run driver for
these exchange rates. Third, in all countries higher geopolitical risk leads to a long-run
depreciation of domestic currency. These results are consistent with the findings in the broad
literature on the political risk and exchange rate nexus. For instance, Manasse et al. (2020)
found that the increased risk of Brexit weakened the pounds sterling. Suleman (2017) found a
similar outcome in that higher political risk reduces exchange rate returns. A comprehensive
political risk index used by Bouraoui and Hammami (2017) and Bahmanee-Oskooee (2019)
was also shown to undermine the value of the domestic currency.
We highlight the following as our main contributions to the literature. First, we link the
geopolitical risk index of Caldara and Iacoviello (2019) to studies on exchange rates which
hitherto has not been attempted at the time of writing. While there have been many studies on
how the geopolitical index affects other aspects of economic development (e. Apergis et al.,
2018; Antonakakis et al., 2017; Kotcharin and Maneenop, 2020), we enrich the debate further
by linking geopolitical risk to foreign exchange markets. Second, we provide estimates of
long-run elasticities of exchange rates in regard to the said index which can be useful in
gauging the long-term implications of political events. This complements similar studies that
used different proxies of political risks, such as Manasse et al. (2020), Bouraoui and Hammami
(2017) and Bahmanee-Oskooee et al. (2019). Third, we assess the exchange rates of emerging
economies in the ASEAN region which have not been thoroughly covered in the past.
Previous literature either analyse other countries or provide a sweeping view in terms of
panel data analyses (e. Suleman, 2017), which makes it harder to draw policy lessons for
specific economies.
We organise the paper in the following manner. In the next section, we present and review
the relevant literature that we intend to draw on and contribute to. After that, we describe our
data and methodology, before presenting the key results and findings and the relevant policy
implications. Following this, the paper concludes.
2. Literature review
2 Theoretical and empirical literature
Studies on exchange rate determinants have grown rapidly with the theoretical discoveries
such as the monetary approach (Frenkel, 1976; Dornbusch, 1976; Woo, 1985), purchasing
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The results indicate a negative relationship between political risk and real exchange rates.
Moreover, there are also threshold effects in that the relationship between politics and real
exchange rates strengthen beyond a particular threshold.
Abid and Rault (2020) measure political risk by the Economic Policy Uncertainty Index
developed by Baker et al. (2016), and attempt to demonstrate the effects of policy uncertainty
on exchange rate volatility using monthly data (Jan 2003–Dec 2018) of emerging economies.
In a panel VAR, the authors find that local and foreign policy uncertainties affect exchange
rate volatility. This is a follow up to the earlier study by Krol (2014), who also found that
policy uncertainty resulted in more volatile exchange rate fluctuations.
Manasse et al. (2020) develop an empirical form of the exchange model which is estimated
using daily data from 27 May 2015 to 23 June 2017. Various exchange rates are considered
with the British Pounds (GBP) as the numeraire. Exchange rates are regressed on political
risk premium and Brexit probabilities using Dynamic OLS (DOLS). The main finding in this
study is that the higher the Brexit probability, the bigger the depreciation of the GBP.
The research on the impact of political risk on foreign exchange markets should be seen in
the wider context of the literature on political risk and economic performance. Using a GMM
model, an early study by Aisen and Veiga (2006) examined and found a positive relationship
between political factors (proxied by the number of cabinet changes and government crises
between 1960 and 1999) and inflation in a panel of 100 countries. Aisen and Veiga (2013)
extended their earlier study by enlarging the dataset to 169 countries covering 1960–2004.
Using the same proxies of political factors, the authors found that political factors had a
negative impact on per capita GDP growth. Barugahara (2015) studied the impact of politics
(measured by state failure and state fragility index) on inflation volatility in a group of
African countries in the period of 1985–2009, and found that politics significantly increased
inflation volatility. The general conclusion that can be drawn from the literature is that
political risk and uncertainty/instability is detrimental to growth, development and investor
confidence. Goswami and Panthamit (2020) find that increasing political uncertainty lowers
trade flows between Thailand and its trade partners. GaoYan (2020) offers evidence that
lower political risk in host countries is associated with higher outwards direct investments
from China, indicating that political risk affects business decisions in a significant way.
2 Implications for research
The literature uses panel data and time series econometric frameworks on a mix of annual,
quarterly, monthly and even daily data. Also, there are many indicators for political risk or
uncertainty, namely the ICRG indicators of political stability (Suleman, 2017), the Economic
Policy Uncertainty index (Abid and Rault, 2020) and other non-propriety indicators of
political instability customised and originally constructed by some authors (e. Manasse
et al., 2020).
In this paper, we analyse how geopolitical risk (GPR) index of Caldara and Iacoviello (2019)
affects exchange rates in Indonesia, Malaysia, Thailand, Philippines and South Korea in the
short- and long-run. Based on the theoretical and empirical literature, we hypothesise that
any form of political instability (internal or geopolitical) is likely to have a negative impact on
investors’ confidence in the affected country, and thus weaken the domestic currency. To our
knowledge, this is the first paper to use the GPR index to assess exchange rate behaviour for
these countries.
Notably, the GPR [4] index is a monthly index that is constructed based on a count of
words relating to political tensions in 11 leading international newspapers starting from 1985
[5]. These words cover explicit mention of geopolitical risk, acts of war terrorism and actual
occurrences of major geopolitical events. While the source of words come from newspapers in
Western countries instead of Asian newspapers, we argue that any major geopolitical event
with massive proportions in any region (including Asia) would have been picked up by any
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international press. So there is no loss of coverage for Asian events, even though we are
referring to Western media for source of information.
This GPR index has been widely used in empirical research since its inception. For
instance, Apergis et al. (2018) apply the index to assess whether geopolitical factors can
predict the stock returns of 24 global defence firms. They find that the GPR index could only
predict the volatility of stock returns but not the average returns. Antonakakis et al. (2017)
discover that the GPR index undermines the returns, volatilities and time-varying covariance
of stock and oil prices in a large sample size of 118 years Aysan et al. (2019) assess the link
between GPR index and the returns and volatilities of Bitcoins and find a contrasting result in
that geopolitical risk increases both returns and volatility of Bitcoin prices. In another study
with tourism applications, Akadiri et al. (2020) analyse the interrelationship between GPR
index, tourism and economic growth in Turkey, with the key findings showing that
geopolitical risk undermines both economic growth and tourism in the short- and long-run.
These studies utilise Vector Autoregressions (VARs) and non-causality frameworks in their
analyses.
Lee et al. (2020) estimated a tourism demand function with GPR index as one of the
regressors in a dynamic panel of 16 countries. While the authors also attempted a non-
causality analysis, their highlight is the cointegrating equation for tourism demand and the
role played by GPR in affected demand for tourism. The authors found that higher GPR risk
reduced tourism demand. Bouras et al. (2018) attempted a GARCH model of stock market
returns and volatility of 18 emerging market countries with GPR index as a regressor and
found that GPR had only a weak effect on volatility. In a panel study of more than 100
shipping companies, Kotcharin and Maneenop (2020) studied the impact of GPR on corporate
cash holdings and found that cash holdings increased significantly especially for firms with
financial constraints. Mei et al. (2020) developed Mixed Data Sampling (MIDAS) models to
examine the capability of GPR index in forecasting oil future price volatilities. These studies
prioritise the use of single equation types of models with GPR as the explanatory variable to
augment standard regression specifications.
3. Data and methodology
3 Methodology
This study investigates the impact of geopolitical risk on exchange rates and falls in the
range of literature on what determines exchange rates. It is only appropriate to begin this
investigation in the context of existing and well-accepted models of exchange rate behaviour
and to build on these models further. In this regard, the literature presents us with various
models including the PPP model (Tsurumi and Chen, 1998) of nominal exchange rates
(equation 5), the flexible price monetary model of Frenkel (1976) and Bilson (1978) (equation
2), the sticky price monetary model of Dornbusch (1976) (equation 1) and the real interest rate
differential monetary model of Frankel (1979) (equation 3). Equations (1), (2) and (3) are
expressed in a similar form to Zakaria and Ahmad (2009). Equation (4) is attributed to
Frommel et al. (2005), who added a long-term interest rate variable to the specification in
Equation (1).
To extend these well-established models, we augmented them with the GPR index as an
additional regressor, in a similar manner as Bahmanee-Oskooee et al. (2019) in their study of
the impact of political risk on exchange rates. The long-run equilibrium specifications of the
models are presented below while the variables used are summarised in Table 1:
DCU=USDt ¼ β 0 þ β 1 PRODt þ β 2 MONt þ β 3 IRt þ β 5 GPRt (1)
DC=USDt ¼ f 0 þ f 1 PRODt þ f 2 MONt þ f 4 INFLt þ f 5 GPRt (2)
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cointegrating relationships between exchange rate and political factors (holding other things
unchanged), which is the nature of our investigation.
3 Data
Presently the study considers four ASEAN countries namely Indonesia, Malaysia,
Philippines and Thailand. We would have included Singapore as well but the GPR index
does not cover the country.
(1) Indonesia
Data for this study come from various sources and cover January 1998 to July 2019.
The proxy for production in Indonesia (domestic country) is the Manufacturing index
(2010 5 100) obtained from IMF’s IFS. Proxy for production in US is the Manufacturing index
(2012 5 100) obtained from the Federal Reserve Bank of St Louis Database (Fred). Proxy for
production in EU and UK are also the Manufacturing index (2015 5 100) sourced from Fred.
While the absolute differentials may not be 100% accurate, the variations in these
differentials over time gives very consistent results even if other production indicators (such
as GDP) are used.
Money supply in Indonesia is M2 (Mil IDR), obtained from the Central Bank of Indonesia.
Meanwhile, the money supply for US (Bil USD) and EU (Mil EUR) are also M2 and sourced
from Fred and ECB respectively. UK money supply is M4 (Mil GBP) and obtained from the
Bank of England. Interest rate data in Indonesia is the 3-month interbank rate (Central Bank
of Indonesia), while the US and UK interest rates are represented by the 3-month Treasury bill
rate and 3-month interbank rate respectively (both sourced from Fred). The EU interest rates
is the 3-month EURIBOR rate (from European Money Market Institute). There are no long-
term interest rate indicator for Indonesia since the data is incomplete. Hence, we leave out
Equation (4) for Indonesia.
Inflation rate in Indonesia, US, EU and UK are all CPI inflation rates sourced from the BIS
database of price and inflation rates. Finally, the exchange rates are spot, nominal bilateral
exchange rates (middle rates) covering IDR/USD, IDR/EUR and IRD/GBP (from Central Bank
of Indonesia). Importantly, the Geopolitical Risk Index (GPR) is obtained from Caldara and
Iacoviello (2019). With the exception of inflation rates and interest rates, all data are log-
transformed [6].
(2) Malaysia
Data covers January 1998 to July 2019. Proxy for production in Malaysia is the Industrial
Production Index (2015 5 100), available from the Monthly Statistical Bulletin (MSB) of the
Central Bank of Malaysia. Proxies for production in US, UK and EU are their respective
Industrial production indices (US: 2012 5 100, UK: 2015 5 100, EU: 2015 5 100) – all available
from Fred.
Proxy for money supply in Malaysia is M2 and for interest rates we use 3-month time
deposit rates, taken from various issues of the MSB. For US, UK and EU, the money supply
and interest rate data are the same data as the one used to estimate the model for Indonesia.
Long term interest rates in Malaysia is the 1-year yield for Malaysian Government Securities
obtained from MSB issues. For US, UK and EU, the long term interest rates are measured by
the 12-month interbank offer rates obtained from Fred.
Inflation rate in Malaysia, US, EU and UK are all CPI inflation rates sourced from the BIS
database of price and inflation rates. Finally, the exchange rates are spot, nominal bilateral
exchange rates covering MYR/USD, MYR/EUR and MYR/GBP (from Central Bank of
Malaysia). The Geopolitical Risk Index (GPR) is obtained from Caldara and Iacoviello (2019).
(3) Philippines
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Data covers January 1996 to July 2019. Proxy for production in Philippines is the Volume
Production Index for Manufacturing (2015 5 100), available from the MISSI. Proxies for
production in US, UK and EU are identical to those used in the Indonesia model.
Proxy for money supply in Philippines is M2 and for interest rates we use 61–90-day time
deposit rates, taken from Central Bank of Philippines. Money supply proxies, interest rates
and data sources for US, UK, and EU are the same as in the Malaysia and Indonesia models.
Long term interest rate in Philippines is the 180-days to 1 year-deposit rates, whereas the
counterpart rates in US, EU and UK are the same as the ones used for the Malaysia model.
Inflation rate in Philippines, US, EU and UK are all CPI inflation rates sourced from the BIS
database of price and inflation rates. Finally, the exchange rates are spot, nominal bilateral
exchange rates covering PHP/USD, PHP/EUR and PHP/GBP (from Central Bank of
Philippines). The Geopolitical Risk Index (GPR) is obtained from Caldara and
Iacoviello (2019).
(4) Thailand
Data runs from January 1997 to July 2019. Production is measured by the Manufacturing
production index (2010 5 100) sourced from the Office of Industrial Economics Thailand.
Proxies for production in US, UK and EU are identical to those used in other country models.
Proxy for money supply is Broad Money (sourced from Bank of Thailand) and for interest
rates we use 3-month average commercial bank deposit rates (also sourced from Bank of
Thailand). Money supply and interest rate proxies and data sources for US, UK and EU are
the same as in the other country models. Long term interest rate is the 1-year Treasury bond
rate from Bank of Thailand, whereas the counterpart rates in US, EU and UK are the same as
the ones used for other country models.
Inflation rate in Thailand, US, EU and UK are all CPI inflation rates sourced from the BIS
database of price and inflation rates. Finally, the exchange rates are spot, nominal bilateral
exchange rates covering TBH/USD, THB/EUR and THB/GBP (from Bank of Thailand).
The Geopolitical Risk Index (GPR) is obtained from Caldara and Iacoviello (2019).
4. Findings and discussions
We first report the descriptive statistics in Table 2. All variables are log-transformed, except
for inflation and interest rate differentials. All the countries considered here have very similar
averages and volatilities for geopolitical risk. This is perhaps unsurprising since they are
located within close proximity to China, whose growing political and economic clout
determines the balance of power in the region. All the countries’ exchange rates are also
equally volatile. Notably, the interest rate differential, inflation differential and price volatility
are exceptionally large in Indonesia, but this is due to the effects of the Asian Financial Crisis.
Thailand and Malaysia in comparison are relatively low inflation countries, albeit the
inflation differentials are still positive. Each country also has positive interest rate
differentials, indicating that the domestic rates are relatively higher than the foreign
interest rates.
We also run some preliminary checks on the correlation coefficients between the exchange
rates of each country and the explanatory variables used the exchange rate models to get a
sense of how the explanatory variables may be related to the dependent variable
(see Table A1 in Appendix). It is important to bear in mind that correlation analysis only
looks at a pair of variables. On the other hand, the multiple regression framework adopted
here incorporates multiple interactions between all variables. Hence, the results suggested by
a pairwise correlation analysis does not automatically imply the same results as regression
coefficients in a multiple regression framework.
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sample leads to a failure of numerous diagnostics tests, especially the CUSUM and
CUSUMSQ tests of parameter stability. The actual sample used for each country are as
follows Indonesia: June 2010 to July 2019, Malaysia: Sept 2005 to June 2015, Philippines: Jan
2000 to July 2019 and Thailand: Jan 2000–July 2019. Results of the Cointegration tests are
reported in Table 3. We have tried using Markov-Switching models before deciding to use
ARDL. But the Markov Switching models failed to produce consistent and robust results.
We set the maximum number of lags at 12 for the ARDL models, and select the best model
using Akaike Information Criterion (AIC). We find that a lower lag order is insufficient to
capture serial correlation in the data. The results are fairly similar with different information
criterion.
When conducting the bounds test, we compare the computed F-statistic against a set of
critical bounds. There is evidence to reject the null of “No Cointegration” if the F-statistic
exceeds the upper bound critical value. On the other hand, the null of no Cointegration is not
rejected if the F-statistic is smaller than the lower bound critical value. Ambiguity arises if the
F-statistic lies between the upper and lower bound, in which case one cannot conclude
whether Cointegration exists until the order of integration for the variables are established
using unit root tests.
In line with Narayan and Smyth (2005) and Narayan and Narayan (2005) who were some
of the pioneers of the ARDL approach, we applied both the 5 and 10% significance levels in
the bounds tests. The results in Table 2 suggest strong evidence of Cointegration in Indonesia
and Malaysia. For the Philippines there is strong evidence of cointegration in models 2 and 3
at 5% significance level but cointegration is only detected for other models at 10% level.
There is relatively weaker evidence of Cointegration for Thailand since the F-test rejects the
null of no Cointegration for only three equations. The reasons for failure to detect
cointegration in model 4 and 5 in Thailand could be due to the failure of the PPP (purchasing
power parity) condition to hold (Jiranyakul and Batavia, 2009) and the lack of sensitivity of
exchange rates to interest rate changes Bouraoui and Phisuthtiwatcharavong (2013).
Hence, exchange rates and the other explanatory variables are long-run driving factors for
nominal exchange rate behaviour.
Where evidence of cointegration is detected in the bounds test, we can then calculate the
long-run coefficients of the cointegrating equations (1) to (5) [7]. The technical details of these
models are explained in Section 2 of the Appendix at the end of the paper. These coefficients
are reported in Tables 4–7.
The bottom sections of these tables contain the results of standard including serial
correlation (Breusch-Godfrey LM test), heteroscedasticity (Breusch-Pagan-Godfrey test),
non-normality (Jarque–Bera test), ARCH effects and model misspecification. (Ramsey
RESET test). These diagnostic tests were passed satisfactorily at 5% level of significance.
Moreover, we also did additional diagnostic checks (but not reported) by calculating the
Ljung–Box Q-statistic for residuals and squared residuals and performed the CUSUM and
CUSUMSQ test of structural stability – these additional tests were passed also and detailed
results are available upon request.
To clarify the interpretation of the results, the nominal exchange rate in each country is
defined as the price of foreign currency in terms of domestic currency. So, an increase in the
numerical value of the exchange rates implies depreciation or weakening of domestic
currency.
In the case of Indonesia, the GPR coefficient is positive and statistically significant at 5%
level for equations (2) to (4), with the coefficient size ranging from 0 to 0. Similar result is
obtained for Malaysia, but the coefficient size is within the range of 0–0. Furthermore,
the GPR coefficient is significant for all four equations. Thus, we conclude that geopolitical
risk and the nominal bilateral exchange rates are positively related for Malaysia and
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Indonesia – a higher geopolitical risk leads to a depreciation of the Indonesian Rupiah and
Malaysian Ringgit.
In the case of Philippines and Thailand, the GPR coefficients are also positive, similar to
Malaysia and Indonesia. These coefficients are statistically significant at 5 and 10% for
Philippines and Thailand respectively. Although cointegration is detected in all models for
Philippines and models 1–3 for Thailand as reported in Table 2, we report only the best
models in Table 3 due to the fact that the other models failed the parameter stability tests and
are thus unreliable to be interpreted.
These results are consistent with the findings in the broad literature on the political risk
and exchange rate nexus in the sense that political risk tends to harm investors’ confidence in
the domestic economy resulting in weaker domestic currency value. The theoretical and
empirical framework of Manasse et al. (2020) suggest that greater political uncertainty raises
Indonesia
Equation
Maximum lag length
Critical bounds at 5% significance
Critical bounds at 10% significance
Computed F-statistic
(1) 12 (2, 3) (2, 3) 5** (2) 12 (2, 3) (2, 3) 9** (3) 12 (2, 3) (2, 3) 11** (4) n (2, 3) (2, 3) n (5) 12 (2, 3) (2, 3) 10**
Malaysia
Equation
Maximum lag length
Critical bounds at 5% significance
Critical bounds at 10% significance
Computed F-statistic
(1) 12 (2, 3) (2, 3) 3** (2) 12 (2, 3) (2, 3) 3** (3) 12 (2, 3) (2, 3) 3** (4) 12 (2, 3) (2, 3) 3* (5) 12 (2, 3) (2, 3) 3**
Philippines
Equation
Maximum lag length
Critical bounds at 5% significance
Critical bounds at 10% significance
Computed F-statistic
(1) 12 (2, 3) (2, 3) 3* (2) 12 (2, 3) (2, 3) 4** (3) 12 (2, 3) (2, 3) 4** (4) 12 (2, 3) (2, 3) 3* (5) 12 (2, 3) (2, 3) 3*
Thailand
Equation
Maximum lag length
Critical bounds at 5% significance
Critical bounds at 10% significance
Computed F-statistic
(1) 12 (2, 3) (2, 3) 3** (2) 12 (2, 3) (2, 3) 3* (3) 12 (2, 3) (2, 3) 3** (4) 12 (2, 3) (2, 3) 2. (5) 12 (2, 3) (2, 3) 2.
Note(s): *, ** and *** represent statistical significance at 10%, 5% and 1% respectively. For Indonesia, data for long-term interest rates are not available, so we are not able to estimate equation (4) for this country
Table 3. The results of the Bounds Test for Cointegration
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Dependent variable: MYR/USD
Equation
a/
Independent variables
####### (1)
####### (2)
####### (3)
####### (4)
####### (5)
####### PROD
####### 0(0)
#######
####### 0.
####### (0)
#######
####### 0.
####### (0)
#######
####### 0.
####### (0)
####### –
####### MON
#######
####### 0***(0)
#######
####### 0***(0)
#######
####### 0**(0)
#######
####### 0**(0)
####### –
####### IR
####### 0***(0)
####### –
#######
####### 0.
####### (0)
#######
####### 0.
####### (0)
####### –
####### GPR
####### 0***(0)
####### 0**(0)
####### 0***(0)
####### 0***(0)
####### 0***(0)
####### INFL
####### –
####### 0(0)
####### 0(0)
####### –
####### –
####### P
####### –
####### –
####### –
####### –
#######
####### 0.
####### (0)
####### P*
####### –
####### –
####### –
####### –
####### 0(1)
####### LTIR
####### –
####### –
####### –
####### 0(0)
####### –
Constant
#######
####### 0**(0)
####### 0***(0)
#######
####### 0**(0)
#######
####### 0*(0)
####### 2(2)
####### ECM
t-
#######
####### 0***(0)
#######
####### 0***(0)
#######
####### 0***(0)
#######
####### 0***(0)
#######
####### 0***(0)
Diagnostic Tests for the presence of:
b/
Serial correlation
####### F(12,62)
####### 5
####### 0.
####### (0)
####### F(12,71)
####### 5
####### 0.
####### (0)
####### F(12,58)
####### 5
####### 0.
####### (0)
####### F
####### (12,39)
####### 5
####### 1.
####### (0)
####### F(12)
####### 5
####### 0.
####### (0)
Heteroskedasticity
####### F(35,74)
####### 5
####### 1.
####### (0)
####### F(27,83)
####### 5
####### 0.
####### (0)
####### F(39,70)
####### 5
####### 0.
####### (0)
####### F
####### (66,51)
####### 5
####### 0.
####### (0)
####### F
####### (37,80)
####### 5
####### 0.
####### (0)
Non-normality
Jarque
- Bera
####### 5
####### 1.
####### (0)
Jarque
- Bera
####### 5
####### 0.
####### (0)
Jarque
- Bera
####### 5
####### 0.
####### (0)
Jarque
- Bera
####### 5
####### 5.
####### (0)
Jarque
- Bera
####### 5
####### 1.
####### (0)
####### ARCH(12)
####### F(12,85)
####### 5
####### 0 (0)
####### F(12,85)
####### 5
####### 1 (0)
####### F(12,85)
####### 5
####### 0(0)
####### F(12,93)
####### 5
####### 0 (0)
####### F(12,93)
####### 5
####### 0 (0)
Model misspecification
####### F(1,73)
####### 5
####### 0 (0)
####### F(1,82)
####### 5
####### 0 (0)
####### F(1,69)
####### 5
####### 1(0)
####### F(1,50)
####### 5
####### 1 (0)
####### F(1,79)
####### 5
####### 2 (0)
Note(s):
a/
*, ** and *** represent statistical significance at 10%, 5% and 1% respectively. Standard error in parenthesis
b/
p
-values in parenthesis
Table 5. Long-run coefficients for Malaysia
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1555
the risk premium and leads to domestic currency weakening in the context of UK Suleman
(2017) found a similar outcome in that higher political risk reduces exchange rate returns in a
panel dataset of developed and developing countries. A comprehensive political risk index
used by Bouraoui and Hammami (2017) was also shown to undermine the value of the
domestic currency in the Arab Spring countries. Meanwhile, Krol (2014) and Abid and Rault
(2020) found that greater political risks leads to higher exchange rate volatility. Our findings
extend these studies in three ways. First, we use of the geopolitical risk (GPR) index as a
measure of political risk. Second, we provide estimates of long-run elasticities of exchange
rates in regard to GPR. Third, we cover some developing countries that have not been
thoroughly covered before in the past.
5. Practical implications
There are some important implications arising from our findings. First, it makes sense for
financial market investors and portfolio managers in Indonesia, Malaysia, Philippines and
Thailand to go short on the domestic currency and long on USD when local geopolitical
conditions are deteriorating. This arbitrage strategy would help to take advantage of the
expected long-run depreciation of the domestic currency value relative to the USD. Similarly,
such strategies are also appropriate for hedging purposes in the light of the findings here,
Dependent variable: PHP/USD Equationa/ Independent variables (2) (3)
PROD 0* (0)
####### 0**
####### (0)
####### MON 0**
####### (0)
####### 0**
####### (0)
####### IR – 0.
####### (0)
####### GPR 0**
####### (0)
####### 0**
####### (0)
####### INFL 0*
####### (0)
####### 0**
####### (0)
####### P – –
####### P* – –
####### LTIR – –
Constant 0. (1)
####### 0.
####### (1)
ECMt-1 0*** (0)
####### 0***
####### (0)
Diagnostic Tests for the presence of: b/ Serial correlation F(12,198) 5 0 (0)
####### F(12,195) 5
####### 0(0)
Heteroskedasticity F(24,210) 5 1 (0)
####### F(27,207) 5
####### 1(0)
Non-normality Jarque–Bera 5 5. (0)
Jarque–Bera 5 4. (0) ARCH(12) F(14,206) 5 1. (0)
####### F(12,210) 5 1.
####### (0)
Model misspecification F(1,209) 5 0. (0)
####### F(1,206) 5 0.
####### (0)
Note(s): a/ *, ** and *** represent statistical significance at 10%, 5% and 1% respectively. Standard error in parenthesis b/ p-values in parenthesis
Table 6. Long-run coefficients for Philippines
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6. Concluding remarks
The ASEAN region is known to be a politically strategic if not volatile area due to its
geographic position and historical role in international politics. Hence, questions regarding
the implications of geopolitical risks on economic performance in ASEAN countries would
arise naturally. One aspect of economic performance is the behaviour of asset markets
particularly the value of foreign exchange. There is a growing literature dedicated to this
branch of research, to which our study makes a contribution.
Our paper examines the impact of political risks on exchange rate behaviour in a number
of ASEAN countries, namely Indonesia, Malaysia, Thailand and the Philippines.
Theoretically, more political uncertainty provides incentives to investors to adjust their
portfolios by moving away from domestic currency to holding “safe” foreign currencies. This
suggests a positive relationship between geopolitical risk and nominal exchange rate which
we define as the price of foreign currency in terms of domestic currency – more geopolitical
uncertainty tends to weaken domestic currency value.
We tested the impact of geopolitical risks on exchange rates using a dataset of monthly
observations of nominal exchange rates, geopolitical risk index (GPR) and other control
variables. Among our salient findings, there is firstly cointegration in the exchange rate
models of Indonesia, Malaysia, Philippines and Thailand when the domestic currency value
of USD is used as the dependent variable. Second, geopolitical risk is a significant long-run
driving force behind these exchange rate movements. Third, higher geopolitical risk leads to
a long-run depreciation of the domestic currencies relative to the USD. This supports the view
that more geopolitical uncertainty drives investors away from domestic currency holdings
into foreign currency holdings (in this case, USD) thereby weakening the domestic currency
value. The results hold across all countries we are considering here and are robust to
alternative model specifications.
Our results extend the current literature in several directions. First, at the time of writing
there are not many studies linking the geopolitical risk (GPR) index of Caldara and Iacoviello
(2019) to foreign exchange markets. There are studies utilising this index to assess the
implications of geopolitical risk on other aspects of economic development such as Apergis
et al. (2018) and Antonakakis et al. (2017) who studied how GPR index affect the stock markets
and Kotcharin and Maneenop (2020) who examined GPR effects on cash holdings of
corporations.
Second, we provide estimates of long-run elasticities of exchange rates with respect to
geopolitical risk proxied by the GPR index. This complements similar studies that used
different proxies of political risks, such as Manasse et al. (2020), Suleman (2017), Bouraoui and
Hammami (2017) and Bahmanee-Oskooee et al. (2019) who found that political risk was
associated with domestic currency depreciation. Third, we provide in-depth assessments on
the exchange rates of emerging economies in the ASEAN region which have not been
thoroughly covered in the past. Existing literature either analyse other countries
(e. Bouraoui and Hammami, 2017 in the Arab Spring countries) or provide a sweeping
view in terms of panel data analyses (e. Suleman (2017) and Bahmanee-Oskooee et al. (2019))
which makes it harder to draw policy lessons for specific economies. Our results confirm the
prior beliefs that political risks are detrimental to confidence in the foreign exchange markets.
In view of the findings, we believe that financial market players, governments and also
central banks could leverage upon strategies that take advantage of the negative effects that
geopolitical risks could have on the foreign currency returns.
Notes
- ASEAN stands for the Association of South East Asian Nations, comprising ten member countries in the Southeast Asian region. The member countries currently include Malaysia, Indonesia, Singapore,
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Thailand, Philippines, Brunei, Vietnam, Laos, Myanmar and Cambodia. The group was formerly established on 8 August 1967 with the main purpose to promote growth and development to combat the spread of communism. The founding members were Malaysia, Indonesia, Singapore, Thailand and Philippines. The other members joined much later. Member countries subscribe to a method of working known as the “ASEAN way” that attempts to solve issues in a consultative and non-confrontational way, emphasizing compromise and non-interference in member countries’ internal affairs.
For instance, the spat between Indonesia and Malaysia over the alleged mistreatment of Indonesian domestic worker in Malaysia, and also disputes between Singapore and Malaysia over the low price of water supplied by Malaysia to Singapore.
An example would be the dispute between Malaysia and Singapore over the Pedra Branca, a set of islands close to the maritime borders of both countries.
URL: matteoiacoviello/gpr.htm; and alternative link is here: https://www. policyuncertainty.com/gpr
These include The Boston Globe, Chicago Tribune, The Daily Telegraph, Financial Times, The Globe and Mail, The Guardian, Los Angeles Times, The New York Times, The Times, The Wall Street Journal, and The Washington Post.
Money supply data are originally reported in different currencies. To facilitate comparability when taking money supply differentials, the domestic (Indonesia) money supply is converted into foreign currencies, using the PPP exchange rate, defined as the ratio of domestic CPI to foreign CPI. Also, the money supply differential between Indonesia and US are in different units of measurement – so Indonesia money supply is scaled to “billions” before the differentials are taken. While there may be concerns about the dependence between the independent and dependent variables, this problem is minimized as the dependent variable and PPP exchange rates are defined differently. Moreover, we are using the ARDL bounds test approach to estimate the models; as this approach can remedy for endogeneity problems this issue is not a major concern (Tang, 2004). Also, the money supply differential between Indonesia and US are in different units of measurement – so Indonesia money supply is scaled to “billions” before the differentials are taken.
The details of the re-parameterisation can be found in Pesaran et al. (2001) and Tang (2004), and also explained in the Appendix.
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Appendix
Section 1: Correlation analysis of variables used in study Section 2: Parameterisation of the Autoregressive Distributed Lag (ARDL) Model – Long-run coefficients, Error Correction Forms and Bounds Test The following explanation is adapted from the EViews User Guide, which is based on the original work of Pesaran et al. (2001). Let “yt” represent the dependent variable, which is the nominal bilateral exchange rate in this study and let “xj” represent “k” explanatory variables (i. the determinants of exchange rates). A general ARDL(p, q1, q 2... qk) formulation of the exchange rate model can be expressed as follows:
yt ¼ a 0 þ a 1 t þ
Xp
i¼ 1
ψi y t−i þ
Xk
j¼ 1
Xqj
l j ¼ 0
βj;lj xj;t−lj þ u t (A1)
where a 1 , ψi and βj;lj are the coefficients of linear trend, lags of yt and the “k” regressors respectively for
j 5 1, 2.. ., k. Equation (A1) can also be written as
ψðLÞy t ¼ a 0 þ a 1 t þ
Xk
j¼ 1
βjðLÞxj;t þ u t (A2)
where “L” is the lag operator such that Ly t 5 yt-. There are several alternative expressions of this general form. For instance, solving y in terms of x gives the long-run relationship between y t and x j:
yt ¼ ψ− 1 ð 1 Þ a* 0 þ a* 1 t þ
Xk
j¼ 1
βjð 1 Þxj;t þ
Xk
j¼ 1
β* j ðLÞΔxj;t þ u* t
!
(A3)
¼ α 0 þ α 1 t þ
Xk
j¼ 1
Θjð 1 Þxj;t þ
Xk
j¼ 1
Θ ~jðLÞΔxj;t þ ξt
where a
0 ¼ a 0 ψ~ð 1 Þψ
− 1 ð 1 Þa
1
β* j ðLÞ ¼ βjðLÞ ψðLÞψ− 1 ðLÞβjðLÞ
u* t ðLÞ ¼ u t ψ~ðLÞψ− 1 ðLÞΔu t
Indonesia Malaysia Thailand Philippines
PROD 0 0 0 0. MON 0 0 0 0. IR 0 0 0 0. GPR 0 0 0 0. INFL 0 0 0 0. P 0 0 0 0. P* 0 0 0 0. LTIR – 0 0 0. Note(s): Coefficients for each country correspond to the same samples used for running the regressions
Table A1. Correlation coefficients between the nominal exchange rates and each explanatory variable
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10-1108 Ijoem-08-2020-1001
Course: Economic (944)
University: Kolej Tingkatan Enam Tunku Abdul Rahman Putra
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