Factors Influencing International Reserves

Author:
Dr. Mohammad Kashif
Assistant Professor,
School of Commerce & Management
IIMT University, Ganga Nagar,
Mawana Road, Meerut, India
1
Copyright © 2021, Author. All rights reserved.

Introduction
• World holdings of total international reserves have increased sharply in the recent
years. Total international reserves were valued at 5.2 trillion US$ in 2006 and
reached their peak of near about 12.83 trillion US$ as on January’ 2018 (IMF).
• Developing economies significantly increased their reserve holdings in last decade
after the financial crisis. Developing countries hold reserves as a buffer stock to
smooth unexpected and temporary imbalances in international payments.
• There is growing debate about the need to hold large stockpiles of reserves. It has
been argued that large stockpiles are needed to forestall currency and financial
crisis that are frequent and severe in today’s international monetary system.
• The trend of reserves accumulation is occurring without regard to its diminishing
marginal benefits and rising marginal costs. This led to the debate on determinants
and influencing factors of the international reserves.
2

Components of International Reserves
3

• Globalization and liberalization spawned financial integration of the world economies.
• Financial globalization or financial integration is an aggregate concept that refers to
increasing global links generated through cross-border financial flows and a country's
linkages to foreign exchange market.
• The wave of financial globalization has been marked by a surge in capital flows
among countries and more notably between developed and developing countries.
• Foreign exchange market is considered as the most dynamic market in domestic as
well as global economy.
• The market is called to be in disequilibrium if the demand for international currency
exceeds supply of it or vice versa.
• In the situation of disequilibrium, the central bank or monetary authority of the
concerned economy intervenes in the market to bring out desired balance by using
international reserves.
• central banks also keep the international reserves to forestall against financial
upheaval.
4
Introduction contd..

Review of Literature
Sr.
No.
Topic No. of
articles
reviewed
Authors
1 Determinants of
IRs
19 Coppin (1994); Chin-Hong et al. (2011); Delatte & Fouquau
(2011); Irefin & Yabaa (2012); Flood & Marion (2002); Ball &
Reyes (2005); Flood & Jeanne (2000); Alam & Rahim (2013).
2 Demand for IRs 23 Aizenman & Jaewoo (2007); Bahmani-Oskooee & Brown
(2002); Edwards & Sebastian (1985); Dooley & Folkerts-
Landau (2003); Eichengreen & Mathieson (2000).
3 Global
imbalances and
Financial stability
55 Chinn et al. (2011); Ben-Bassat & Gottlieb (1992); Bird &
Rajan (2003); Jingting & Kalemli-Ozcan (2013); Smaghi
(2010); Huang (2006); Obstfeld et al. (2010); Obstfeld &
Rogoff (2009).
4 Others 39 Aizenman et al. (2014); Gokhale et al. (2013); Nneka (2012);
Gray (2011); Cheung & Ito (2007); Romero (2005); Baksay et
al. (2012).
Total 136 5

Research Gap
Continuing debate is going on the large hoarding of international
reserves over the last decade. Still no consensus available on the
topic.
 The rapid growth of international reserves remains a puzzle (Obstfeld, M. et al., 2010);
 Reserves accumulation in emerging markets is difficult to justify (Jeanne, O., 2005);
 The higher rate of reserves accumulation is the outcome of both precautionary and mercantilist motives
(Malloy, M., 2013).
The continuing debate on the factors influencing international reserves
reflects the need for further evidence.
Much literature is available for research conducted on the motives of
holding international reserves- precautionary or mercantilist by taking bulk
number of countries irrespective of differentiating them into developed and
developing ones.
6

Research Gap contd..
With a view to fill this gap, this study is done as follows:
Examining the factors influencing international reserves and find the causes of
accumulation of international reserves.
For this purpose, ten reserve holding countries have been selected where the large
accumulation of international reserves is concentrated. The data for these
economies are analysed based on time series data properties.
7

Objective of the study
To examine the factors influencing the international
reserves of selected countries.
8

Summary of Research Methodology
Objectives Variables used Methodology Software used
Objective 1
Dep. Variable: International reserves
minus gold.
Ind. Varibales: Econ; Imp; Reer;
Topen.
1. Econometric tools. ADF and PP
Unit root, Johansen cointegration;
Error correction mechanism.
2. ARDL Bounds testing approach.
MS-Excel; E-
views 8 & 8.1.
Microfit 4.1 & 5.
9

# Objective #
To examine the factors influencing
the international reserves of selected
countries.
10

Data Sample and model specification
 An essential Time series data for the study have been collected from international financial statistics database of the
IMF, world development indicators database of the World Bank and OECD countries database.
 The empirical analysis covers overall ten top reserves holding countries over the period of 1985Q1 to 2014Q4.
 The estimation process involves overall five variables namely total international reserves (IR), economic growth
(Econ), vulnerability to external shocks (Imp), real effective exchange rate (Reer) and trade openness (Topen).
 The study developed following model relating demand for international reserves to the independent variables:
IR = f ( Econ, Imp, Reer, Topen) (1)
 The explicit form of the model is:
IRt = β0 + β1Econt + β2Impt + β3Reert + β4Topent + εt (2)
 The logarithmic form of our model is:
Ln(IR)t = β0 + β1Ln(Econ)t + β2Ln(Imp)t + β3Ln(Reer)t + β4Ln(Topen)t + εt (3)
Research Methodology contd…
11

The study employed Augmented Dickey Fuller (Dickey & Fuller, 1979) and Phillips
Perron (Phillips & Perron,1988) test to check stationarity of each variable.
12
∆𝑌𝑡 = 𝑎𝑜 + 𝜆𝑦𝑡−1 + 𝑎2𝑡 +
𝑖=1
𝑝
𝛽𝑖∆𝑦𝑡−𝑖+1 + 𝜀𝑡
∆𝐼𝑅𝑡 = 𝑎𝑜 + 𝜆𝐼𝑅𝑡−1 + 𝑎2𝑡 +
𝑖=1
𝑝
𝛽𝑖∆𝐼𝑅𝑡−𝑖+1 + 𝜀𝑡 ∆𝑇𝑜𝑝𝑒𝑛𝑡 = 𝑎𝑜 + 𝜆𝑇𝑜𝑝𝑒𝑛𝑡−1 + 𝑎2𝑡 +
𝑖=1
𝑝
𝛽𝑖∆𝑇𝑜𝑝𝑒𝑛𝑡−𝑖+1 + 𝜀𝑡
∆𝑅𝑒𝑒𝑟𝑡 = 𝑎𝑜 + 𝜆𝑅𝑒𝑒𝑟𝑡−1 + 𝑎2𝑡 +
𝑖=1
𝑝
𝛽𝑖∆𝑅𝑒𝑒𝑟𝑡−𝑖+1 + 𝜀𝑡 ∆𝐼𝑚𝑝𝑡 = 𝑎𝑜 + 𝜆𝐼𝑚𝑝𝑡−1 + 𝑎2𝑡 +
𝑖=1
𝑝
𝛽𝑖∆𝐼𝑚𝑝𝑡−𝑖+1 + 𝜀𝑡
∆𝐸𝑐𝑜𝑛𝑡 = 𝑎𝑜 + 𝐸𝑐𝑜𝑛𝑡−1 + 𝑎2𝑡 +
𝑖=1
𝑝
𝛽𝑖∆𝐸𝑐𝑜𝑛𝑡−𝑖+1 + 𝜀𝑡

13
Variables Augmented Dickey Fuller (ADF) Phillips-Perron (PP)
Level Decision 1st diff. Decision Level Decision 1st diff. Decision
LnIR 0.17 Accepted -10.38* Rejected -0.20 Accepted -10.39* Rejected
LnEcon -0.99 Accepted -3.11+ Rejected -1.07 Accepted -4.99+ Rejected
LnImp -1.63 Accepted -2.91+ Rejected -0.86 Accepted -5.21* Rejected
LnReer -2.10 Accepted -8.85* Rejected -2.17 Accepted -9.37* Rejected
LnTopen -1.87 Accepted -4.28+ Rejected -1.31 Accepted -5.24* Rejected
The study adopted the method of co-integration suggested by Johansen &
Juselius (1992) to set up the long-run relationship between international
reserves and its determinants.
H0: unit root exists in the time series.

Johansen & Juselius (1992) anticipated two likelihood ratio statistics namely- trace
statistics and the max-eigen value statistics. The significant eigen values of Π determine
the number of co-integrating vector(s) with the help of these statistics. These statistics
examine the null hypothesis of r = 0 against r > 1 to determine the number of vector(s).
Johansen & Juselius co-integrating analysis comprises the following model:
Where, Yt represents vector of non-stationary variables. Γ, Π, and λ denotes matrices of
parameters which are to be estimated.
Now, it should be verified whether the variables have an intrinsic tendency to move
together in the long-run or not by applying Johansen's co-integration test.
14
Δ𝑌𝑡 = Π𝑌𝑡−1 +
𝑖=1
𝑘−1
Γ𝑖 Δ𝑌𝑡−𝑖 + 𝜆 𝐷 + 𝜀𝑡

H0: Trace
statistics
Critical
values
P-values Decision Max-Eigen
value
Critical
values
P-values Decision
r = 0 102.91+ 69.82 0.00 Rejected 42.27+ 33.88 0.00 Rejected
r = 1 60.64+ 47.86 0.00 Rejected 26.18* 24.71 0.07 Rejected
r = 2 34.46+ 29.80 0.01 Rejected 21.82+ 21.13 0.04 Rejected
r = 3 12.64 15.50 0.13 Accepted 11.79 14.26 0.12 Accepted
r = 4 0.85 3.84 0.36 Accepted 0.85 3.84 0.36 Accepted
15
Trace statistic and max-eigen value shows co-integration; + and * denotes rejection of null hypothesis at 5% and 10% level.
Having found long-run relationship among the variables, we proceed the
estimation process by first reporting the co-integrating regression of the long
run relationship.
Johansen's co-integration test results:

The estimated long- run relationship between international reserves and its determinants
is given below:
IRt = -10.553 + 1.502 Econt + 1.046 Impt - 0.298 Reert + 0.145 Topent + εt
(-5.004)
*
(15.397)*
(3.905)
+
(-1.099) (0.435)
R2 = 0.95 F-stat = 505.86; p-value = 0.00 D-W statistic = 0.29
Note: Figures in parenthesis are t-statistics.
* and + denotes significant at 1% and 5% level.
16
The low Durbin-Watson statistics indicates possible spurious regression. But if the
residuals of this model are found stationary then this model will no longer be spurious
(Gujrati, D. 2003).

Stationarity of residuals implies that variables are co-integrated suggesting for error
correction mechanism proposed by Engle & Granger (1987).
Based on the representation theorem suggested by Engle and Granger (1987), the
specification of the error correction model that we want to estimate takes the following form:
ΔlnIRt = α0 + βi ΔlnIRt−1 + γi ΔlnEcont−1 + δi ΔlnImpt−1 + ψi ΔlnReert−1 + ϕi ΔlnTopent−1 + λ ECTt−1 + εi
Where all variables are defined as before and ECT is the error correction term.
The estimated error correction model is as under:
∆IRt = 0.145 + 1.029 ∆LnEcon – 1.252 ∆LnImp – 0.214 ∆LnReer + 0.833 ∆LnTopen– 0.169 ECT
(0.93) (2.88)+ (-1.76)# (-1.11) (1.13) (-3.46)+
Note: Figures in parenthesis are t-statistics.
+and # denotes significant at 5% and 10% level.
17

Diagnostic check:
18
Test Statistics p-value Conclusion
Breusch-Godfrey Serial Correlation LM Test 2.98 0.23 No serial correlation
White Heteroskedasticity Test 2.56 0.78 No heteroskedsticity
Ramsey RESET Test 3.39 0.14 No misspecification
Jarque-Bera Normality test 11.13 0.21 Normally distributed

The same methodology has been applied to the countries of which the variables show
the same order of integration when augmented Dickey-Fuller and Phillips-Perron unit
root tests used.
However, where the variables don't show same order of integration the autoregressive
distributed lag (ARDL) approach will be applied (Pesaran, Shin & Smith, 2001).
 We found following countries’ variables with same order of integration- Brazil, Italy,
Korea rep, Mexico and Switzerland. The consolidated results are:
19
Country LnIR LnEcon LnImp LnReer LnTopen
Level 1st diff Level 1st diff Level 1st diff Level 1st diff Level 1st diff
Italy -2.45 -17.13* -1.77 -3.94+ -1.12 -4.24+ -2.25 -7.77* -1.06 -4.07+
Mexico -0.70 -8.64+ -2.55 -3.75+ -1.73 -3.43+ -2.29 -9.54* -1.23 -3.49+
Switzerland 1.45 -3.12+ 0.09 -3.27+ 0.09 -5.16* -2.03 -9.08* 0.87 -3.01+
Korea rep -2.52 -9.40* -2.17 -5.33* -0.43 -3.32+ X X -0.63 -3.00+
ADF Test- H0: unit root exists in the time series. H0 be rejected @ 1st diff.

Phillips-Perron test:
20
Country LnIR LnEcon LnImp LnReer LnTopen
Level 1st diff Level 1st diff Level 1st diff Level 1st diff Level 1st diff
Italy -5.2 -16.93* -3.77 -4.98* -1.38 -6.73+ -2.00 -7.75+ -1.06 -5.99*
Mexico -0.75 -8.60* -0.76 -4.70+ -2.96 -5.56+ -2.81 -9.65+ -1.96 -5.02+
Switzerland 1.12 -10.22* -2.76 -4.99* 0.34 -5.14+ -2.42 -9.18+ 0.82 -4.84+
Korea rep -2.26 -9.46* -2.22 -5.32* -0.45 -5.51+ X X -0.68 -5.60+
H0: Unit root exists in the time series.
H0 be rejected @ 1st diff. in each case

Country Trace Statistics Max-eigen value
r = 0 r = 1 r = 2 r = 3 r = 4 r = 0 r = 1 r = 2 r = 3 r = 4
Italy
73.20*
39.52 21.52 7.91 0.77
33.68+ 17.99
13.61 7.14 0.77
Mexico
169.35+
92.60+ 34.01* 9.20 2.48
76.75+ 58.59+
24.81* 6.72
2.48
Switzerland
79.55*
44.33 27.80 14.97 5.46*
35.22* 16.53
12.83 9.51
5.46*
Korea rep
119.39+
65.37+ 32.61* 10.22 0.49
54.02+ 32.75*
22.39* 9.73
0.46
Critical value 69.82 47.86 29.80 15.50 3.84 33.88 27.58 21.13 14.26 3.84
Consolidated Johansen's co-integration test results:
21
* and + denotes rejection of null hypothesis at 1% and 5% significance level.

Country Regressors
LnEcont LnImpt LnReert LnTopent Constant
Coeffic. t-Stats Coeffic. t-Stats Coeffic. t-Stats Coeffic. t-Stats Coeffic. t-Stats
Italy 0.51 4.05* -2.63 2.40+ 1.78 2.18+ 2.32 1.68+ 0.40 0.11
Adjusted R-squared = 0.68 F-statistics = 11.18 (0.00) D-W statistics = 0.99
Mexico 1.61 13.10* -1.30 2.45+ -0.74 2.12+ 1.65 2.97+ -14.27 5.25*
Switzerland 0.23 1.49 0.84 0.59 6.88 11.99* 2.23 1.86+ -11.59 4.77*
Korea rep 2.02 30.89* -3.84 5.44* X X 4.25 6.85* -29.51 14.46*
22
Long run estimates: (Dependent variable LnIRt)

Short run estimates: Error correction model (Dependent variable LnIRt):
23
Country Regressors
Ln𝚫Econt Ln𝚫Impt Ln𝚫Reert Ln𝚫Topent Error
correction term
Italy 0.25 0.30 -2.85 0.80 1.69 1.13 3.29 0.77 -0.49 6.01*
R2 = 0.24 D-W statistics = 2.11
Mexico 0.86 1.86+ -0.51 0.59 0.13 0.37 0.55 0.50 -0.17 3.09+
Switzerland 1.17 3.52+ -3.87 1.95+ 2.05 3.68+ 4.53 2.19+ -0.09 1.99+
Korea rep 0.80 2.43+ -2.06 2.48+ X X 2.03 2.30+ -0.07 1.97+

Diagnostic Tests:
24
Country Test
Serial Correlation Heteroskedasticity Ramsey RESET Normality
Stats
(P.value)
Decision Stats
(P.value)
Decision Stats
(P.value)
Decisio
n
Stats
(P.value)
Decision
Italy 18.48
(0.11)
No 11.72
(0.92)
No 6.34
(0.00)
No 67.21
(0.35)
Normally
distributed
Mexico 11.62
(0.09)
No 5.75
(0.33)
No 0.83
(0.01)
No 78.4
(0.43)
Normally
distributed
Switzerland 0.67
(0.71)
No 9.56
(0.97)
No 0.19
(0.03)
No 22.51
(0.37)
Normally
distributed
Korea rep 2.34
(0.31)
No 7.58
(0.09)
No 3.81
(0.00)
No 19.87
(0.57)
Normally
distributed

25
-40
-30
-20
-10
0
10
20
30
40
86 88 90 92 94 96 98 00 02 04 06 08 10 12 14
CUSUM 5% Significance
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1998 2000 2002 2004 2006 2008 2010 2012 2014
CUSUM of Squares 5% Significance

ARDLApproach
In the empirical research on time series data, there is a problem of non-stationarity
which renders the traditional tools of econometrics like OLS, 2SLS inappropriate.
To overcome this problem, the co-integration approach has been developed ( Engle &
Granger 1987 and Johansen1990,1992).
However the basic condition for co-integration analysis is that the variables in the
system should be non-stationary at levels and stationary of the same order.
When another five countries were analysed, the unit root tests indicated that the
variables under study were not found integrated of the same order.
To overcome this problem, (Pesaran, Shin & Smith 1999, 2001) developed a bounds
testing autoregressive distributed lag (ARDL) procedure that ignores the order of
integration.
26

 The demand for international reserves to the independent variables is expressed as:
IR = f ( Econ, Imp, Reer, Topen) (1)
 We transform all the variables into natural log form and posit the following estimable model:
Ln(IR)t = β0 + β1Ln(Econ)t + β2Ln(Imp)t + β3Ln(Reer)t + β4Ln(Topen)t + εt(2)
 To capture the speed of adjustment, we estimate the following error correction model:
ΔlnIRt = α0 + βi ΔlnIRt−1 + γi ΔlnEcont−1 + δi ΔlnImpt−1 + ψi ΔlnReert−1 + ϕi ΔlnTopent−1 + λ ECTt−1 + εi
(3)
 To implement the bounds testing procedure, it is essential to model equation (2) as a conditional ARDL as:
∆LnIRt = α0 + ∑ βi ∆LnIRt-i + ∑ γi ∆LnEcont-i+ ∑ δi ∆LnImpt-i + ∑ θi ∆LnReert-i +
∑ψi∆LnTopent-i + λ1LnIRt-i+ λ2LnEcont-1 + λ3LnImpt-1 + λ4LnReert-1
+ λ5LnTopent-1 + εt(4)
27

Bounds test for Co-integration:
28
Bounds testing critical values of the F-statistics: intercept and no trend
90 percent level 95 percent level 99 percent level
k I(0) I(1) I(0) I(1) I(0) I(1)
4 2.45 3.52 2.86 4.01 3.74 5.06
Calculated F-statistics:
Algeria FIR (IR/Imp, Reer, Topen) = 6.035*
FImp (Imp/IR, Reer, Topen) = 4.802
FReer (Reer/IR, Imp, Topen) = 3.364
FTopen (Topen/IR, Reer,Imp) = 3.787#
China FIR (IR/Econ, Imp, Reer, Topen) = 3.595#
FEcon (Econ/IR, Imp, Reer, Topen) = 2.046
FImp (Imp/Econ, IR, Reer, Topen) = 0.179
FReer (Reer/Econ, IR, Imp, Topen) = 3.169
FTopen (Topen/Econ, IR, Reer,Imp) = 0.488

India FIR (IR/Econ, Imp, Reer, Topen) = 4.21+
Japan FIR (IR/Econ, Imp, Reer, Topen) = 1.50
FEcon (Econ/IR, Imp, Reer, Topen) = 3.65#
FImp (Imp/Econ, IR, Reer, Topen) = 4.14+
FTopen (Topen/Econ, IR, Reer,Imp) = 4.06+
Germany FIR (IR/Econ, Imp, Reer, Topen) = 2.43
FReer (Reer/Econ, IR, Imp, Topen) = 3.73#
29
*, + and # denotes 1%, 5% and 10% significance level. Critical values are extracted from Pesaran et al. (1999)

Country Regressors
LnEcont LnImpt LnReert LnTopent Constant
Algeria
(2,0,1,1)
X X -8.65+ 2.02 -1.99 1.14 13.38* 3.31 29.79* 3.85
China
(2,0,0,0,0)
1.34* 18.1 -2.63+ 3.42 0.49 1.24 3.86* 4.39 -12.38* 7.39
India
(1,2,0,0,0)
-1.42 0.99 -12.01+ 1.99 2.66 0.66 17.16 1.30 47.69 1.43
Japan
(2,0,1,2,1)
-1.79 1.63 -7.85+ 2.04 1.89 1.09 6.55# 1.89 55.67+ 2.28
Germany
(1,0,0,1,0)
0.98* 3.80 4.94+ 2.24 -1.77 1.17 -6.03+ 3.12 9.41+ 3.97
30
Long run estimates of the ARDL model :

Country Regressors
Ln𝚫Econt Ln𝚫Impt Ln𝚫Reert Ln𝚫Topent Constant ECT
Coeffic. t-Stats Coeffic. t-Stats Coeffic. t-Stats Coeffic. t-Stats Coeffic. t-Stats Coeffic. t-
Stats
Algeria
(2,0,1,1)
X X -0.38+ 2.96 -0.79+ 2.78 -1.07 2.09 1.13+ 2.52 -0.04+ 3.29
China
(2,0,0,0,0)
0.26* 4.39 -0.51+ 2.85 0.09 1.14 0.76+ 3.41 -2.41 3.38 -0.20* 4.69
India
(1,2,0,0,0)
1.5+ 2.64 -0.90 1.58 0.19 0.79 1.28+ 2.49 3.56 1.28 -0.07# 1.89
Japan
(2,0,1,2,1)
-0.10+ 1.99 0.98+ 2.14 0.41+ 3.41 -0.82 1.63 3.15+ 2.39 -0.06+ 2.56
Germany
(1,0,0,1,0)
0.24+ 2.87 1.22+ 2.12 1.11# 1.85 -1.47+ 2.72 2.32+ 2.08 -0.25* 4.32
31
Error correction estimates of the ARDL model :

Diagnostic check:
32
Country Test
Serial Correlation Heteroskedasticity Ramsey RESET Normality
Stats
(P.value)
Decision Stats
(P.value)
Decision Stats
(P.value)
Decision Stats
(P.value)
Decision
Algeria 0.13
(0.88)
No 2.76
(0.09)
No 2.72
(0.00)
No 20.31
(0.39)
Normally
distributed
China 0.63
(0.53)
No 0.88
(0.57)
No 3.32
(0.02)
No 51.08
(0.12)
Normally
distributed
India 0.06
(0.93)
No 0.90
(0.54)
No 0.57
(0.04)
No 1.15
(0.56)
Normally
distributed
Japan 0.60
(0.66)
No 1.01
(0.46)
No 2.12
(0.05)
No 26.93
(0.13)
Normally
distributed
Germany 0.53
(0.71)
No 1.53
(0.22)
No 8.85
(0.00)
No 62.30
(0.23)
Normally
distributed

Summary & Findings
In developed countries the most significant factor in the long run is trade openness.
Economic growth and vulnerability to external shocks also determine these
reserves to a lesser extent.
In developing countries the most determining factor are vulnerability to external
shocks and economic growth.
Trade openness also determines the reserves to a lesser extent.
The results reveal that accumulation of international reserves in developed
economies is due to mercantilist motive.
Furthermore, developed economies also have the fear of financial crisis and hence
hold a sufficient amount of international reserves for future exigencies and to
provide confidence to foreign investors.
The results confirm that accumulation of international reserves in developing
economies is due to precautionary purposes.
Since trade openness is found significant, therefore, having large stockpiles of
reserves seem reasonable by developing countries.
33

Thank You Very Much
34

Factors Influencing International Reserves

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