Short Term Portfolio Investment and BI
Rate: Do They Determine the
Stabilization of Rupiah Exchange Rate in Indonesia?
Chenny Seftarita^{1}, Fitriyani^{2[*]},
Cut Zakia Rizki^{3}, Diana Sapha^{4},
and Abd. Jamal^{5}
^{1,2,3,4,5} Development Economics Department, Faculty of Economics and Business,
Universitas Syiah Kuala, Aceh, Indonesia
Abstract. This study
aims to investigate the influence of shortterm portfolio
investments and BI interest rate on fluctuation of rupiah exchange rate in
Indonesia. The data used is quarterly data from 2010 to 2016 collected from
Indonesia Central Bank. Using the Autoregressive Distributed Lag (ARDL) method,
the result showed that rupiah exchange rate was strongly influenced by shocks
in the private debt securities, joint stock price index, and BI Rate, both in
the long run and short run. Moreover, it is found that there was a shortrun
and longrun balance relationship between Short Term Portfolio Investments and
BI rate against the rupiah exchange rate. Thus, it is recommended that in order
to stabilize the exchange rate, it is necessary to maintain the stability of
shortterm portfolio investments.
Keywords: Short Term Portfolio Investments,
Nominal Exchange Rate, BI Rate, ARDL
JEL
Codes: C32, E42, G11
How
to cite: Seftarita, C., Fitriyani,
F., Rizki, C., Sapha, D.,
& Jamal, A. (2019). Short Term Portfolio Investment and BI Rate: Do They
Determine the Stabilization of Rupiah Exchange Rate in Indonesia?.
Journal of Economic Development, Environment and People, 8(1), 1828. doi:http://dx.doi.org/10.26458/jedep.v8i1.608
1.
Introduction
Volatility
in the exchange rate has severely happened in Indonesia in the last few years
after the global crisis in 2008. As an open economy country, Indonesia faces a
problem regarding the weakening of the exchange rate. Rupiah exchange rate
against the US Dollar continued to depreciate and reached its peak in 2018,
where the rupiah was Rp15,329 per US dollar, shown in Figure 1.
Some
of the factors expected to affect the exchange rate include shortterm
portfolios investment such as debt securities and shares. Change in private
debt was expected to affect the exchange rate fluctuation. Moreover, “Hot
money”, was also prone to cause the changes in the rupiah exchange rate,
particularly when there was a significant decline in the composite stock price
index in 2013 and 2015 (Figure 2.)
Many
studies have examined the relationship between shortterm portfolio investments
and the rupiah exchange rate. Ibarra (2011) analyzed the influence of capital
flows on the appreciation of RER in Mexico with ARDL bound test approach. The
results found that the capital inflows caused peso to be appreciated between
1988 and 2008. The influx of foreign capital into the country from various
forms, including; foreign debt, portfolio investment
in the form of stocks and bonds, foreign trade surplus, and the others were
effective on strengthening the domestic currency. Figure 3 illustrate the
comparison between Rupiah Exchange Rate and IDX (Indonesian Composite Index)
from 2010 to 2016. When the private debt experienced a drastic decline in the maturity
of payments, the rupiah exchange rate would be depreciated, and vice versa.
Similarly,
Uddin, Rahman, & Quaosar (2014) also examined the
factors influencing exchange rate fluctuations in Bangladesh with the cointegration
approach. It was stated that macroeconomic variables influenced the Bangladesh
currency in the end. The increase in debt, both private debt and government
debt cause depreciation in the exchange rate, in contrast, a rise in foreign
exchange reserves caused a currency appreciation. In addition, not only
economics factors, but also social factors such as political issue had a
negative impact on domestic exchange rate fluctuations.
In
addition, the interest rate is also one of variables that is important in
explaining the shock of the exchange rate. Foreign interest rates greatly
affected the industrialized countries and eventually affected the economy of a
country. Furthermore, the effectiveness of the influence of foreign interest
rates was strongly influenced by the regime of the exchange rates adopted by a
country (Giovanni & Shambaugh, 2008).
Likewise,
Wu & Xia (2016) examined the effect of monetary variables on exchange rates
in AsiaPacific by using the Markov Switching Model (MSM). The Asia Pacific
country used a varied exchange rate system and generally set its exchange rate
with US dollars. After the 1997/1998 Asian crisis, the exchange rate was hard
to be controlled so that the exchange rate tended to be volatile. The variables
that determine the change in the exchange rate include money supply, discount
rate, and industrial production.
Moreover,
Elahi, Salimi, & Masoomzadeh
(2016) believed that the inflation Targeting Framework was very instrumental in
maintaining the stability of exchange rates and trade balance in the countries.
Some other fundamental variables greatly influence exchange rate fluctuations,
such as gross domestic product, interest rate, money supply, and inflation.
Therefore,
this paper aims to examine the effect of shortterm portfolio investments and
BI rate on exchange rates in Indonesia.
It is remarkable to address this issue, as Indonesia is one of the
countries with a highly volatile exchange rate where Indonesia is adopted
freefloating exchange rate system.
2.
Literature Review
2.1.
ISLM
Model
In the ISLM model, money supply and money
demand are equal. On the supply side, M/P as the real money supply is equal to
the demand that is the total number of transactions in the economy (Y). Demand
for money, in this case, is strongly influenced by the interest rate. The
interest rate is an opportunity cost for the choice of holding bonds or holding
money. The change in interest rate is heavily influenced the investor's
decision to make an investment (Blanchard, 2009)
M/P = Y L (i)........................................................................................................................................(1)
In the open financial markets, investors
face the choice of whether to hold domestic or foreign assets. The choice is
assumed that financial investors, both domestic and foreign, will choose an
investment that will produce the highest expected rate of return. The
difference in domestic and foreign interest rate greatly influences the
investor's decision. The interest parity condition can be written (Blanchard,
2009):
Where
This condition shows that domestic and
foreign interest rates together with the expected exchange rate are strongly
influenced the real exchange rate. Increasing in domestic interest rates will
strengthen the exchange rate while rising in foreign interest rates will weaken
the exchange rate. The expected exchange rate will also greatly affect the real
exchange rate (Blanchard, 2009).
3.
Methodology
This study uses a quarterly time series data of
exchange rate, shortterm private debt, BI Rate, and Composite Stock Price
Index (IDX) from 2010 to 2016. The data are collected from Indonesia Central
Bank. AutoRegressive Distributed Lag (ARDL) bounds test method is being
utilized to address the main objective issue.
Pesaran & Shin (1995) explained that the
ARDL procedure have two steps. Firstly, it is to estimate the longrun
relationships among the variables. Estimation can be performed using Ftest
that is the fundamental in assessing the longrun relationship. If the value of Fstatistic is greater than
the upper bound value, then the null hypothesis is rejected and can be
concluded that there is no cointegration and, hence the longrun relationship
runs among the variables. Meanwhile, if the value of the Fstatistic is lower
than the upper bound values, we do not reject the null hypothesis and assumed there
is no long run relationship among variables. The second, it is to determine the
coefficients of the longrun relationship. ARDL Model used in this study can be
written as:
Where; EXR is
the nominal exchange rate; PRIV_DEBT is shortterm private debt; BIRATE is
interest rate of Indonesia Central Bank that reflects the domestic interest
rate; IDX is Composite Stock Price Index;
4.
Results and Discussion
4.1.
Unit Root
Test
Shrestha &
Bhatta (2018) explains that if a value of time series data has
a tendency to return to its average value in the longrun, and are not
changed in time, then a data is called stationary. Nevertheless, if a value of
time series data does not return to its average value in the longrun, then it
is called nonstationary, which means that the variance and covariance are not
constant and changing over time. Many of the macroeconomic variables including
inflation and exchange rate are not stationary. Gujarati (2009) said that if
the estimation is conducted with the existence of unit root then the result of
the estimation would be spurious regression. Spurious regression is defined as
a regression that produces a biased conclusion that indicates the relationship
of the variables is meaningless, for example, the R^{2} values would result in higher percentage even if
the data is not correlated.
There are many
types of unit root test included Augmented DickeyFuller (ADF), PhillipsPerron, KPSS, etc. Unlike other
tests, the null hypothesis in KPSS test is trend stationary and the alternative
hypothesis is nonstationary, which means that there is a presence of a unit
root in the model (Naiya & Abdul Manap, 2013).
From
the results of the unit root test using KPSStest, all variables used in this
model are stationary. The exchange rate, the interest rate of the Indonesian
central bank and private debt are stationary at first difference, I(1). On the other hand, the composite stock price index
variable is stationary at level, I(0) (Table 1).
4.2.
Optimal
Lag
Iriobe, Obamuyi, & Abayomi (2018) explained that before the
ARDL bound test is conducted, the next step of ARDL model estimation step
is to determine the optimal lag length of each variable by using Akaike's
Information Criterion (AIC) or Schwarz Bayesian Criterion (SBC). Lag length is
used as the basis for estimating shortterm and longterm variables. In this
study, we use the least Akaike's Information Criterion (AIC) value to estimate
the bestfitted ARDL model. From these results in Table 2, the selected ARDL
model is in lag (3, 2, 0, 0).
Table 2 describes that IDX is significantly affected
the rupiah exchange rate at a confidence level of 5 to 10 percent. Meanwhile,
other variables such as BI rate and shortterm private debt do not significant
affecting the exchange rate.
Figure 4 shows the lag length criterion using AIC.
There are 20 selected models, and the ARDL model in lag (1, 3, 0, 0) is the
best fitted model used in this study. After that, the autocorrelation testing
is done by looking at the residual test using the Q Colleogram
table as shown in Table 3 below. From the results of the residual UI, the
pvalue is not significant at the confidence level of 5 to 10 percent, which
means that the autocorrelation does not exist in this model.
4.3.
Parameter
Consistency Testing
Do
& Zhang (2016) checked the stability of coefficient using the
cumulative sum of recursive residuals (CUSUM) and the CUSUM of square
(CUSUMSQ), utilized by Pesaran & Pesaran (1997). The plot of the
CUSUM and CUSUM of squares describe about stability of coefficient and the
longrun relationship among variables. If the line of CUSUM stays within the 5%
significance bounds, then we do not reject the null hypothesis assumed that
there is a longrun relationship among variables and thus shows stability of
coefficient, and vice versa, if the plot of CUSUM exceed the 5%, critical
bounds then, the model is instable. With this result, plot of CUSUM in figure 5
do not exceed the 5% critical bounds, and hence we conclude that the
coefficient in this model is stable and have longrun relationships among
variables.
4.4.
ARDL Bound Test
We used ARDL bounds test
approach to investigate the longterm relationships between the variables, namely the
relationship between the BI interest rate, shortterm capital flows and
fluctuations in the rupiah exchange rate. This is important to determine
whether BI rate and changes in portfolio investments are significant in
affecting the longterm exchange rate. We use Ftest to examine this longrun
relationship. Simultaneously, since the Fstatistic value of 8.749687 is above
the lower limit (I0) and upper limit (I1) of the Bounds test (Table 4), then we
conclude that all variables used in this study are cointegrated. This means
that BI rate is assumed to influence the rupiah exchange rate in the long term.
In addition, shortterm fluctuations in capital flows, which are the joint
stock price index and private debt securities also affect fluctuations in the
rupiah exchange rate in the long run.
This result is similar with Uddin, Rahman, & Quaosar
(2014) which concluded that both private debt and government debts are
significant influencing the exchange rate in Bangladesh.
(Shakil, Mustapha, Tasnia,
& Saiti, 2018) explains that If longterm
relationship occurs between the variables, the errorcorrection model (ECM) is conducted
to estimate the balance that occurs between dependent and independent
variables. A negative and significant of ECM value assists the information about the rate of speed adjustment of
dependent variables returns to equilibrium after
shock. A value of 1.261582 (pvalue=0.0001)
assumed the speed of adjustments of the exchange rate to equilibrium the when
there are changes in BI rate and shortterm Portfolio Investments (Table 5).
5.
Conclusion and Recommendation
As an open economy country, Indonesia faces a problem
regarding the volatility of the exchange rate. This paper investigates the
influence of shortterm portfolio and BI interest rate on rupiah exchange rate
in Indonesia. We applied ARDL bound testing and CUSUM
to investigate the long run and short run effect and test the stability of the
model. The result of ARDL bound testing shows that Indonesia currency
was strongly influenced by shocks in the private debt securities, joint stock
price index, and BI Rate. Many studies support this finding, which stated that shortterm
portfolio investments and "Hot Money" because of speculation
activities are very vulnerable causing fluctuations of exchange rate. Hence, it
is suggested for the government to strengthen the stabilization of the
shortterm portfolio investments included private debt, capital flows, and to
control the competitive interest rates. Moreover, many factors can be done to
strengthen the rupiah exchange rate, including enhancing the trade surplus and
strengthening domestic industries.
6.
References
[1]
Bank
Indonesia. www.bi.go.id. 15 12 2018. <www.bi.go.id>.
[2]
Blanchard, Olivier. Macroeconomics,
Fifth Edition. New Jersey: Pearson Education, Inc, 2009.
[3]
Do, Thi Thao and Jian Hua Zhang.
"ARDL Bounds Testing Approach to Cointegration: Relationship
International Trade Policy Reform and Foreign Trade in Vietnam." International
Journal of Economics and Finance 8.8 (2016): 8494.
[4]
Elahi, Naser , Farshid Salimi and
Elahe Masoomzadeh. "Investigating Asymmetric Effects of Monetary Shocks
on the Exchange Rate and Trade Balance, with an Emphasis on Inflation
Targeting." Procedia Economics and Finance 36 (2016): 165176.
[5]
Giovanni , Julian di and Jay
Shambaugh. "The impact of foreign interest rates on the economy: The role
of the exchange rate regime." Journal of International Economics
74.2 (2008): 341361.
[6]
Gujarati, Damodar. Basic
Econometric. New York: McGrawHillIrwin, 2009.
[7]
Ibarra, C. A. "Capital Flows
and Real Exchange rate Appreciation in Mexico." World Development
39.12 (2011): 20802090.
[8]
Iriobe, Grace O., Tomola M. Obamuyi
and Muftau A. Abayomi. "Effect of Foreign Portfolio Investment in Bond
Stocks on the Performance of the Nigerian Stock Market." Archives of
Business Research 6.12 (2018): 164172.
[9]
Naiya, Ismaeel Ibrahim and Turkhan
Ali Abdul Manap. "Structural Transformation, Poverty and Inequality in
Nigeria: An ARDL Bound Testing Technique." International Journal of
Economics and Finance 5.4 (2013): 141152.
[10] Pesaran,
H and B Pesaran. Microfit 4.0 Interactive Econometric Analysis. Oxford:
Oxford University Press, 1997.
[11] Pesaran,
M.H. and Y. Shin . "An Autoregressive Distributed Lag Modelling Approach
to Cointegration Analysis." Cambridge Working Papers in Economics
9514, Faculty of Economics, University of Cambridge. (1995).
[12] Shakil,
Mohammad Hassan , et al. "Is Gold a Hedge or a Safe Haven? An Application
of ARDL Approach." Journal of Economics, Finance and Administrative
Science 23.44 (2018): 6076.
[13] Shrestha,
Min B. and Guna R. Bhatta. "Selecting Appropriate Methodological
Framework for Time Series Data Analysis." The Journal of Finance and
Data Science 4, 7189. 4.2 (2018): 7189.
[14] Uddin,
Kazi Mohammed Kamal , Mohammad Morshedur Rahman and G. M. Azmal Ali Quaosar.
"Causality Between Exchange Rate And Economic Growth In Bangladesh."
European Scientific Journal 10.31 (2014): 1126.
[15] Wu, Jing Cynthia and Fan Dora Xia.
"Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower
Bound." Journal of Money, Credit and Banking 48.23 (2016):
253291.




