Download Management & Engineering Internationalization Based on SVAR Model

Management & Engineering 07 (2012) 1838-5745
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Management & Engineering
Journal Homepage: www.seiofbluemountain.com
Empirical Research on Several Factors
Internationalization Based on SVAR Model
Affecting
RMB
Liping SU, Si WU
School of Economics, Xiamen University, Xiamen 361005, P.R.China
KEYWORDS
RMB internationalization,
SVAR,
Effect
ABSTRACT
Following the US subprime crisis and the European debt crisis, the discussion of RMB’s
internationalization is aroused again. In order to promote the process of RMB
internationalization, it is essential for us to research the factors affecting it. Based on RMB,
we examine the effect of several determinant factors on RMB internationalization via SVAR
econometric model. It turns out that RMB’s exchange rate affects RMB’s internationalization
prominently in lag time. What’s more, China’s GDP generally has a positive impact on RMB’s
internationalization especially in lag time.
© ST. PLUM-BLOSSOM PRESS PTY LTD
1 Introduction
Ever since the end of 2008, RMB’s internationalization entered into a faster-developing phase. Either the openness of experimental
districts in which cross-border trade is settled dominated in RMB, or the sign of currency swap arrangement between China and a
number of foreign countries or districts, or the development of CNH (i.e. offshore RMB market in Hong Kong), or progress of RMB
transaction, or RMB cross-border direct investment, shows the beginning of RMB internationalization.
Other than government’s policies, the determinant factors affecting RMB internationalization are the key to promote it. Among
foreign scholars, Chinn and Frankel (2007) concluded that GDP ratio, inflation, foreign exchange rate and financial development
index are the key factors for a currency to be international reserve. Likewise, Bergsten (1975) argued that the economic conditions
for a currency’s internationalization include economic growth, stable price and comparative advantage in international economy scale.
In China, Li Daokui and Liu Linlin (2008) put forward that internal significant factors affecting a currency’s internationalization
include economic aggregations, inflation, real interest rate, exchange rate appreciation (or depreciation) and its fluctuation range.
Wang Jinsong, Ba Shusong and Yang Xianling (2011) thought that exchange rate has an impact on a currency’s internationalization
through international trade volume, stability of the currency’s value and financial development.
This paper focuses on the analysis of several variables including GDP, inflation rate and nominal effective exchange rate based on
SVAR model, looking for the major factors that affect RMB’s internationalization.

Corresponding author.
E-mail address: [email protected]
English edition copyright © ST. PLUM-BLOSSOM PRESS PTY LTD
DOI: 10.5503/J..ME.2012.07.010
95
2 Selections of Variables and Processing of Data
2.1 Selection of variables
Currency has three functions, i.e. measure of value, medium of exchange and storage of value, on all of which a currency’s
internationalization is depending as well. From the aspect of measure of value, a currency’s internationalization needs its value to be
stable and denomination to be sizable; from the aspect of medium of exchange, a currency’s internationalization depends on the
domestic country’s economy, foreign trade scale and the scope and depth of its financial markets; moreover, from the aspect of
storage of value, a currency’s internationalization is determined by the stability of its internal and external value. Reviewing US
dollar internationalization, we find that increase of economic strength, expansion of export trade, pressure on other currencies and
formation of European markets are the major factors promoting US dollar going internationally. Due to different historical
backgrounds, RMB internationalization is certain not to be the same as that of US dollar, on the contrary, RMB internationalization
needs more economic motivation than political promotion for the latter one is hard to measure and control. Even if different country
has different way to promote its currency to go internationally, in general, they all rely on their strong economic power, stable
currency value and considerable foreign trade scale, based on which this paper tries to verify these factors functions in pushing a
currency’s internationalization. Nevertheless, since RMB internationalization has been started not far from now, there are only a few
sample data for research; as a result, this paper mainly takes into account the two factors of the above three, i.e. economic power and
currency stability. We choose GDP, inflation rate and RMB exchange rate as dependable variables influencing RMB
internationalization, among which inflation rate will be substituted by CPI (Consumer Price Index).
2.2 Preliminary processing of data
In order to build the model, we transfer all the variables into a kind of index form, i.e. relative ratio with fixed base (2010=100). The
specific processes are as follows.
The first one is IR (i.e. Internationalization of RMB). We use the ratio of RMB cross-border trade settlement amount per month
accounting for total export-import volume per month in major economic entities (including the United States, the United Kingdom,
European Union and Japan). For convenience, we consider the average monthly data in 2010 X as a fixed base 100, and make every
monthly data an index which is equal to its ratio accounting for X multiplied by 100 1. RMB cross-border trade settlement amount per
month data come from the monetary policy implementation report on PBC website; the total import and export volume monthly data
in US derive from the US Department of Commerce; the total import and export volume monthly data in UK are from the National
Bureau of Statistics of UK; the total import and export volume monthly data in EU are from the European Union Statistics Bureau;
the total import and export volume monthly data in Japan are from Japan's Finance Ministry.
The second one is GDP (i.e. General Domestic Products). Since there are no GDP monthly data of China, we choose Industrial Value
Added monthly data instead. To be consistent with IR, what we deal with GDP is exactly the same as what we do with IR. Industrial
Value Added monthly data derive from National Bureau of Statistics of China.
The third one is CPI (i.e. Consumer Price Index). CPI monthly data also come from National Bureau of Statistics of China. The
processing is the same as that of IR.
The last one is NEER (i.e. Nominal Effective Exchange Rate). NEER monthly data derive from Bank for International Settlement.
The subsequent processing is alike above.
2.3 Stationarity test
To avoid spurious regression in the process of VAR modeling, it is necessary to analyze the stationarity of all the variables. This
paper tests on the variables in the original form, logarithmic form and first-order difference form of logarithmic one based on ADF
test as well as PP test. The results show that all the variables have non-stationary time series, while all of variables in the third form
have stationary time series. Hence, we set up VAR model based on the original variables’ third form.
3 Modeling
VAR model is a recognized method investigating the dynamic relationships between variables; however, the impulse response
produced by VAR model cannot give structural explanation since it does not consider economic theories. In this paper, we resort to
SVAR model to analyze how changes of RMB exchange rate influence inflation. Compared with unrestricted VAR model, SVAR
model not only considers the endogenous problems within macro-economic variables, but contains the current relationships between
endogenous variables.
1
Specific data process method: first calculate the average monthly data in 2010, and then divide the 29 monthly data between July 2009 and
November 2011 respectively by the average monthly data in 2010, and finally make each of them multiply by 100, after which the index form of data
come out.
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3.1 VAR model lag number selection
Appropriate choice of lag number is vital to a VAR model. Too small lag number influences the dynamic features of the model and
too large one reduces degrees of freedom of the model. We hereby adopt the AIC, SIC and HQ information criteria to determine the
optimal lag number for the model. As shown in table 1, the optimal lag number is 3 for unrestricted VAR model, accordingly, we
construct VAR (3) model.
Table 1 The result of optimal lag number selection
Lag
LogL
LR
FPE
AIC
SC
HQ
0
256.2974
NA
8.69e-15
-21.02478
-20.82844*
-20.97270
1
275.0489
29.68979
7.08e-15
-21.25407
-20.27236
-20.99362
2
282.0152
8.707889
1.71e-14
-20.50126
-18.73418
-20.03246
3
314.5238
29.79958*
6.23e-15*
-21.87698
-19.32453
-21.19982*
4
332.2834
10.35975
1.30e-14
-22.02361*
-18.68580
-21.13809
Notes: The notation * represents the optimal lag number chosen by corresponding criterion.
3.2 About co-integration relationships
Only all variables in the system have a co-integrating relationship can we construct a VAR model. We use the Johansen
co-integration test to examine the five variables in the system, with results shown in table 2.
Table 2 Johansen tests for the number of co-integrating relationships
No of CE(s)
Trace statistics
P value
Max. Eigen
P value
statistics
None
118.0233
0.0000*
44.65769
0.0005*
At most 1
73.36556
0.0000*
33.19335
0.0023*
At most 2
40.17221
0.0002*
28.37356
0.0009*
At most 3
11.79865
0.0610
10.69132
0.0620
At most 3
1.107337
0.3406
1.107337
0.3406
Notes: The notation * shows significance 5% level of significance.
3.3 Test on stability of the model
Since the reciprocal of the mould of the AR model characteristic polynomial roots fall in the unit circle (see figure 1 below), the
model is stable based on which we can analyze the impulse responses.
Inverse Roots of AR Characteristic Polynomial
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Figure 1 The result of stability of the model
3.4 Construction of SVAR model and its constraint conditions
The structures were characterized by IR, GDP, CPI and NEER sequencing. As influence factors of RMB internationalization, GDP,
CPI, and NEER can be classified into two categories; one kind is domestic factors (including GDP and CPI), and another is
international factors (including NEER).
On one hand, from analysis of China's current status in the world economy, the international factors affect domestic factors more than
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reverse condition. Therefore, we mainly consider the international factors’ effect on domestic ones.
On the other hand, GDP influences CPI directly, although CPI will produce certain impact on GDP as well, the influence of the
former situation is apparently stronger, so we only consider GDP influencing CPI.
dlnNEER t = dlnNEER t-1 +ε NEER +a12 ε GDP +a13ε CPI +a14 ε IR
dlnNEER t = dlnNEER t-1 +ε NEER
dlnGDPt = dlnGDPt-1 +a 21ε NEER +ε GDP +a 23ε CPI +a 24 ε IR
dlnGDPt = dlnGDPt-1 -0.19ε NEER +ε GDP
dlnCPIt = dlnCPIt-1 +a 31ε NEER +a 32 ε GDP +ε CPI +a 34 ε IR
dlnCPIt = dlnCPIt-1 +a 31ε NEER +a 32 ε GDP +ε CPI +a 34 ε IR
dlnIR t = dlnIR t-1 +a 41ε NEER +a 42 ε GDP +a 43ε CPI +ε IR
dlnIR t = dlnIR t-1 +a 41ε NEER +a 42 ε GDP +a 43ε CPI +ε IR
ε NEER , ε GDP , ε CPI and ε IR represent structural shock at IR respectively from NEER, GDP, CPI and IR respectively.
Because the SVAR model contains four variables, we need to produce at least (4 * (4-1) / 2 =) 6 constraints. Since RMB exchange
rate mainly changes with reference to a basket of exchange rates, it is less affected by domestic factors, which can be also concluded
from granger causality test. So we suppose a12 = a13 = a14 = 0; As GDP has great influence on foreign trade condition, and vice versa,
through a linear regression between dlnGDP and dlnNEER, we get that dlnNEER’s coefficient a21 is -0.19; meanwhile, we assume
the domestic price and RMB internationalization do not affect GDP, so supposedly a23 = a24 = 0.
4 Impulse Response Analysis and Variance Decomposition on SVAR Model
4.1 Impulse response analysis
In order to describe the dynamic impulse response of each variable to IR, figure 2 depicts 10 periods of the impulse response of
NEER, GDP and CPI to IR respectively after one standard shock at it.
Response to Structural One S.D. Innovations
Response of DLNIR to Shock1
Response of DLNIR to Shock2
10
10
5
5
0
0
-5
-5
-10
-10
-15
-15
5
10
15
20
25
5
Response of DLNIR to Structural
One S.D. Shock3
10
15
20
25
Response of DLNIR to Structural
One S.D. Shock4
.6
.14
.5
.12
.10
.4
.08
.3
.06
.2
.04
.1
.02
.0
.00
-.1
-.02
-.2
-.04
2
4
6
8
10
12
14
16
18
20
22
24
2
4
6
8
10
12
14
16
18
20
22
24
Figure 2 Impulse response of NEER, GDP, CPI and IR to IR
From figure 2 we can see that after a standard positive shock at IR from NEER, IR decreases by 1.90% in the 1 st period, and after
which it decreases further in the 2nd period; afterward, it decreases to the most by 11.6%; whereas after 4 periods of going down, IR
begins to rise and reaches to the top by 6.86%, following which it fluctuates to decrease and the effect vanishes gradually beginning
from the 9th period. It turns out that appreciation of NEER has a negative impact on IR in the early periods which might be aroused
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by its negative effect in domestic economy through the channel of foreign trade. However, in the latter periods NEER’s appreciation
promotes IR’s increase thanks to the strong value of RMB. After a standard positive shock at IR from GDP, IR goes down slightly in
the 1st period but immediately increases by approximately 3% in the 2nd period, followed by the influence fading down gradually and
turning to a negative one, beginning to decrease abruptly to the lowest by 8.98%; then it returns to a positive effect rapidly, reaching
to the top by 9% in the 7 th period and fluctuating to vanish during the last 3 periods. It is concluded that GDP’s effect on IR is not
steady which shows the problems in the corresponding transmission mechanism. After a standard positive shock at IR from CPI, IR
rises and falls greatly in the first six periods. In the 1st period, it increases by 0.59% while falls suddenly in the 2nd period and returns
to a positive increase with less degree. Afterward, it fluctuates positively and negatively and the effect begins to vanish after the 7 th
period. After a standard positive shock at IR from IR itself, it increases in the first five periods and the 2 nd period pushes it to the top
by 0.13%, which states that IR has an impact on itself in the following period. Moreover, the positive influence weakens beginning
from the 2nd period, after which it vanishes little by little.
4.2 Variance deposition
In order to analyze each endogenous variable’s contribution in structural shock, table 3 lists every variable’s contribution rate in
structural shock at IR in each of the first 10 periods.
Table 3 Each variable’s contribution rate in structural shock at IR
Period
Std. Deviation
NEER
GDP
CPI
IR
1
1.000000
89.55565
1.835498
8.608703
0.000149
2
1.391723
93.24522
6.478618
0.264766
0.011401
3
1.396267
96.51731
3.332004
0.143366
0.007318
4
1.687168
96.69500
3.161826
0.136092
0.007077
5
1.716123
75.76100
24.12738
0.106061
0.005565
6
1.778201
71.40617
28.50359
0.085665
0.004574
7
1.807974
60.84222
39.08121
0.072684
0.003884
8
1.808023
60.36912
39.55641
0.070683
0.003788
9
1.810373
57.32027
42.60902
0.067112
0.003597
10
1.818775
57.67805
42.25190
0.066485
0.003573
Table 3 explains different variable’s contribution rate in structural shock at IR in different period. As shown in table 3, from the 1st
period to the 10th period, NEER explains the effect much more than other variables, illustrating that NEER influences IR the most.
Then GDP explains the effect more than the rest two variables, which manifests the close relationship between the status of a
country’s currency and the country’s economic power. Surprisingly, IR explains the least of the effect, from which we know that
RMB’s internationalization is in the initial low-degree developing stage, which pushes itself weakly. Generally, NEER and GDP have
greater impact on IR with the latter one affecting it in the lag periods.
5 Conclusion
This paper resorts SVAR model to research empirically the factors influencing RMB internationalization in the period between July
2009 and November 2011, including RMB’s exchange rate, GDP and inflation. The conclusions are as follows.
Firstly, RMB’s exchange rate has an obvious lag impact on RMB’s internationalization, which means that exchange rate needs to
affect the process of RMB’s internationalization through a series of domestic economic variables. Moreover, since RMB’s exchange
rate is the major factor affecting RMB’s internationalization and the positive effect is in the lag periods by RMB’s exchange rate’s
appreciation, both of which state that RMB’s internationalization asks for RMB’s strong value.
Secondly, it cannot be ignored that GDP has an impact on RMB’s internationalization as well. Although its effect is not steady,
especially in the latter periods, GDP generally has a positive impact on RMB’s internationalization thus it is vital to keep the steady
growth of domestic economy to accelerate RMB’s internationalization.
Finally, domestic price influenced RMB’s internationalization by little degree, whilst it majorly has a positive impact which
manifests that gentle domestic inflation is favorable to promote RMB’s internationalization.
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New York University Press, 1975
[3]. LI Daokui, LIU Linlin. RMB’s Internationalization: Qualitative Research and Policy Analysis. Journal of Financial Research,
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[4]. WANG Jinsong, BA Shusong and YANG Xianling. The Dynamic Effects of Exchange Rate Fluctuation and Foreign Exchange
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