Conclusion
This paper extends the literature analyzing
the impact of financial stress on economic
activity. Earlier studies demonstrated that
financial stress had a negative influence on
the output of industrial in both advanced
and developing economies. However, in
the case of Vietnam, whether financial
stress has an impact on economic
activities has not been studied. Employing
the threshold vector auto-regression,
the paper finds that there exist financial
stress index threshold which divides the
Vietnamese financial stress index into two
regimes: high regime. In the low regime,
FSI has no influence on GAP measured
by industrial production index (represent
for economic activity) while in the high
regime, the positive shock in FSI would
follow by the increase in GAP. Although,
this effect is not significant, the reaction
of economic activity to the change in FSI
does not follow the economic theory. This
usual result might source from the method
that is used to estimate FSI and the
limitation of data. The study’s limitations
suggest that researchers and policy-makers
develop new methods to compute financial
stress and conduct more research on the
impact of financial stress on economic
growth and other economic variables in
Vietnam. The results of these studies will
help Vietnamese policy-makers formulate
macro-prudential policy for a financial
system more comprehensively
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74
Tạp chí Khoa học & Đào tạo Ngân hàng
Số 217- Tháng 6. 2020
© Học viện Ngân hàng
ISSN 1859 - 011X
The effect of financial stress index on the Vietnamese
economic growth - A threshold auto regression approach
Anh Thi Lam Pham
Banking Academy of Vietnam
Ngày nhận: 19/03/2020 Ngày nhận bản sửa: 15/05/2020 Ngày duyệt đăng: 19/05/2020
After the 2008 global financial crisis, financial stress index- an indicator
measuring the instability and risk in financial markets has become one of
the crucial indicators to forecast the financial crisis. Besides the models to
estimate this index, the effect of financial stress on economic variables is the
main topic that the economists focus on researching recently. For Vietnam,
the economy experienced a double crisis in the period from 2008 to 2012.
Besides the high inflation and the sharp decline in economic growth, the
financial market also experienced a high risk and uncertainty period.
Thus, whether there is a link between financial stresses and the decrease in
economic growth in Vietnam is a big question. The study employs threshold
vector auto-regression for the monthly data from 2008 to 2018 to find the
answer to this question. The result indicates the existence of a threshold of
financial stress index and the unusual association between financial stress
and economic growth in Vietnam
Keywords: financial stresses, economic growth, threshold vector auto-
regression, TVAR
Tác động của chỉ số áp lực tài chính đối với tăng trưởng kinh tế của Việt Nam-cách tiếp cận thông qua
mô hình ngưỡng tự hồi quy
Tóm tắt: Sau khi cuộc khủng hoảng tài chính toàn cầu năm 2008, chỉ số căng thẳng tài chính một chỉ số đo
lường sự bất ổn và rủi ro trên thị trường tài chính đã trở thành một trong những chỉ số quan trọng để dự báo
các cuộc khủng hoảng tài chính. Bên cạnh các mô hình để tính toán chỉ số này, tác động của căng thẳng tài
chính đối với các biến số kinh tế là chủ đề chính mà các nhà kinh tế tập trung nghiên cứu. Đối với Việt Nam,
nền kinh tế trải qua khủng hoảng kinh tế suốt trong giai đoạn 2008 đến 2012. Bên lạm phát cao và kinh tế suy
giảm, thị trường tài chính việt nam trong giai đoạn này cũng trải qua rất nhiều rủi ro và bất ổn. Do đó, một câu
hỏi đặt ra ở đây là liệu có mối liên hệ giữa căng thẳng tài chính và sản lượng của nền kinh tế tại Việt Nam hay
không. Nghiên cứu sử dụng hồi quy véc tơ ngưỡng và dữ liệu hàng tháng từ năm 2008 đến hết năm 2018 để
kiểm chứng mối quan hệ giữa hai biến số này. Kết quả cho thấy sự tồn tại của ngưỡng chỉ số căng thẳng tài
chính và mối liên hệ bất thường giữa căng thẳng tài chính và tăng trưởng kinh tế ở Việt Nam.
Từ khóa: Căng thẳng tài chính, Tăng trưởng kinh tế, mô hình ngưỡng tự hồi quy, TVAR
Phạm Thị Lâm Anh
Email: lamanh@hvnh.edu.vn
Học viện Ngân hàng
ANH THI LAM PHAM
Số 217- Tháng 6. 2020- Tạp chí Khoa học & Đào tạo Ngân hàng 75
1. Introduction
After the 2008 global financial crisis
outbreak, financial stress – a concept
measures the instability and risks in
financial markets- have attracted more
and more attention from the researchers.
This is because; financial stress index-
the indicator measuring financial stress
did forecast quite precisely the appeal
of the 2008 financial crisis. Unlike other
financial concepts, different authors give
a different definition of financial stress.
llling and Lu(2006)- the pioneer in the
studying of financial stress interpreted
financial stress as the force affecting
economic activity through instability and
risk in financial markets and institutions.
Hakkio and Keeton (2009) characterized
financial stress as the increase in
uncertainty about the fundamental value
of assets and asymmetry of information as
well as the decline in the demand for risky
and illiquid assets. Balakrishnan et al.
(2011) defined financial stress as a period
of weakened financial intermediation.
Aboura and Royeb (2017) described
financial stress as a combination of
uncertainty and risk perception. Although
the researchers defined financial stress in
various ways, they all measure the degree
of risk of financial stress through four
markets including: the banking sector,
equity market, bond market, and foreign
exchange market for financial stress
index. Therefore, the financial stress index
(FSI) characterizes for the change in the
instability and risk of financial markets.
Besides playing the role of the early
warning signs of the financial crisis,
financial stress affects economic activities.
Aboura and Roye (2017) referred that
financial stress caused to changing the
behavior of private sector investment and
consumption. Paries et al. (2011) indicated
that increases in money market spreads
would decline bank lending, which
directly reduced economic output. David
and Hakkio(2010) explained that financial
stress would cause companies to postpone
their decision of new investment in order
to observe how uncertainty is overcome.
Further, these authors also referred that
the increase in financial stress would make
companies’ financial condition worse
because of the tightening in financial
resources. As a result, firms would reduce
their investment and profit. Thus, the
rising of financial stress would lead the
negative impacts on economic growth
In the case of Vietnam, since 2008 and
2012, the Vietnamese economy faced a
double crisis: inflation crisis and financial
crisis. For inflation, this rate rocketed to
about 20% in 2008 and 2011 and over
10% in 2007 and 2010. Besides inflation,
in the period 2008 to 2011, Vietnam also
faced a financial crisis with severe issues
in banking sectors and the exchange rate
market. From March to August of 2008,
the interest rate climbed to nearly 20%
while the interbank rate also rocketed to
nearly 40%. In 2011, the banking sector
also faced a crisis regarding liquid risk
and the increase in the bad debt of the
banking system. In this year, the exchange
rate market also suffered the big shock
that forced the Vietnamese State Bank
had to depreciate Vietnam Dong 11%.
As a consequence, the growth rate of
the economic decline to 5% while this
number for the period between 2004
and 2007 is 7% to 8%. This fact causes
the authors to question whether or not
there is a linkage between the instability
in the financial market and economic
The effect of financial stress index on the Vietnamese economic growth - A threshold auto
regression approach
Tạp chí Khoa học & Đào tạo Ngân hàng- Số 217- Tháng 6. 202076
activities in Vietnam. In other words,
whether the financial stress affects the
output of the Vietnamese economy?
To reach the final answer, firstly, this
research will summarize some empirical
studies on financial stress and economic
growth. In the next part, employing the
threshold vector auto-regression model,
the paper will estimate the threshold of
financial stress index before assessing the
relationship between financial stress and
output in each regime. Based on the result
of the previous part, the last part will give
some recommendations and end with
some concluding points.
2. Literature review
The impact of financial stress on economic
growth was studied in various aspects.
For the advanced economies, Liu and
St-Amant (2010) used a threshold vector
auto-regression for quarterly data from
1981Q4 to 2006Q4 to assess the effect of
monetary policy on the real economy in
the different scenarios of financial stress
in Canada. The findings pointed out that
in the high financial stress regime, the
Canadian economy would experience
weaker output growth, higher inflation,
and higher interest rates. For the US
economy, Hubrich and Tetlow, (2015),
Davg and Hakkio (2010), Ferrer, et al.
(2018), Galvao and Owyang (2018)
used the different methods to estimate as
well as employed the different model to
analyze the relationship between financial
stress and economic growth. However,
they all reached the same finding that
financial stress had negative effects on
US economic growth. Roye (2013) used
a dynamic approximate factor model to
estimate FSI for Germany and examined
the link between financial stress and
economic growth through the threshold
vector autoregression model. The results
also indicated that high financial stress
had significant adverse effects on output
in Germany. Aboura and Roye (2017)
applied different models- Markov-
Switching Bayesian vector autoregression
(MS-BVAR) for the French financial
stress data; also found that episodes of
high financial stress would lead to lower
economic activity.
Mittnik and Semmler (2013) indicated
further finding in studying the group of
advanced economies (the US and the
five largest EU economies) with multi-
regime vector auto-regression (MRVAR).
Conducting a size-dependent response,
the authors proved that stress –increasing
shocks harmed economic activity in the
high- stress period, then during low stress,
which was only right for a small shock.
When shocks are sufficiently large, in the
high regime, the effect of large negative
shock in financial stress on real activity is
positive and sizable. Despite employing
the different method for the different
economies, these studies all found that
high financial stress index would lead to
lower output growth
For the developing economies, Cevik et
al. (2013) measured the financial stress
index and studied the relationship between
this index and economic activities in five
transition economies, namely Bulgaria,
the Czech Republic, Hungary, Poland, and
Russian. The result refers that there is a
moderate relationship between financial
stress and some variables of economic
activity. Cevik et al. (2016) concluded
that financial stress had caused significant
economic slowdowns after analyzing
the effect of financial stress on economic
ANH THI LAM PHAM
Số 217- Tháng 6. 2020- Tạp chí Khoa học & Đào tạo Ngân hàng 77
activity in five emerging Asian economies.
Tnga and Kwekb (2015) employed a
structural vector autoregression (SVAR)
for ASEAN- 5 economies and found that
an increase in financial stress led to tighter
credit conditions and lower economic
activity in all these countries.
The impact of financial factors on
Vietnamese economic activities also has
been studied in numerous researches. For
the stock market, Vo et al. (2016) studied
the linkage between financial structure
and economic growth in Vietnam. The
authors point out that the stock market
development had litter impact on
economic growth, and this relationship is
a one-way effect from the stock market
capitalization to economic growth. For
the banking sector, Pradhan et al. (2014)
show that in Vietnam, economic growth
led to banking sector development or
economic growth determines the level
of banking sector development. Le and
Pfau (2008), and Vo and Nguyen (2016)
both concluded that banking credit is
the primary monetary transmission
channel in Vietnam. For the exchange
rate market, Le and Pfau (2008) indicate
that the exchange rate channel is one of
the monetary transmissions in Vietnam,
and the real effective exchange rate had
to impact the change in the output of the
Vietnamese economy in the period of
1996Q2 to 2005Q4. By contrast, Vo and
Nguyen (2016) argued that the exchange
rate channel would be weak and almost
non-existent in Vietnam as a consequence
of the government’s intervention in
foreign exchange markets. For stock
market volatility, although Vo (2015), Vo
(2017), and Nguyen and Nguyen (2013)
studied the volatility of the Vietnamese
stock market, these authors did not show
any evidence for the relationship between
the stock market volatility and economic
growth in Vietnam.
Although the effect of financial factors
on economic growth in Vietnam has
been examined, the impact of financial
stress index on economic activity has not
investigated yet. This reason motives the
author to employ threshold vector auto
regression- model.- TVAR to examine the
relationship between these two factors.
3. Methodology
3.1. Model selection
Besides examining the effect of financial
stress on economic growth, the study also
looks for the threshold of financial stress
index for the Vietnamese economy. Thus,
following Liu and St-Amant (2010) and
Roye (2013), this study uses threshold
vector auto regression- TVAR model.
The threshold VAR model with two
regimes is
Zt = α1 + A1Zt + B1(L)Zt-1 + (α2 + A2Zt +
B2(L)Z
t-1
) I(C
t-d
, γ) + εt (1)
Where the vector of variables (Zt) includes
the Zt = (gapt, f sit, intt, cpit) gapt is output
gap, f sit is the financial stress index (FSI),
intt is the interest rate, cpit is the consumer
price index
I is an indicator that equals 1 if the
threshold variable C
t-d
is larger than the
FSI threshold value γ and 0 otherwise.
When I = 0, the relevant coefficients are
α1, A1 and B1(L) whereas represents the
vector of constant, B1(L) represents the
matrix of contemporaneous interaction
The effect of financial stress index on the Vietnamese economic growth - A threshold auto
regression approach
Tạp chí Khoa học & Đào tạo Ngân hàng- Số 217- Tháng 6. 202078
coefficient, represents the matrix of lag
polynomials. When I = 1, the relevant
coefficients are α1 + α2, A1 + A2 and
B1(L) + B2(L). εt represents the vector of
structural innovations.
This paper employs the Tsay (1998)
method to test for the threshold
nonlinearity of the model. This approach
generates the C (d) test statistic following
by the estimation of an arranged
regression. The null hypothesis that the
model is linear: α2 = 0, A2 = 0, B2 = 0.
C (d) follows a chi-squared distribution
with k(pk+qv+1) degree of freedom. In
this case, k and v represent the number of
endogenous and exogenous variables; p
and q are their corresponding lag orders.
When the null hypothesis of linearity is
rejected, this research utilizes a grid search
method and Akaike Information Criteria
(AIC) to find the thresholds.
We utilize the Cholesky ordering for the
shock identification in the VAR model.
The first order is the GAP since GAP is
the slow-moving variable. The second one
is the FSI, and the interest rate is placed
last. This structure is consistent with
the empirical literature, which suggests
that financial stress and monetary policy
indicators are fast-moving market-based
variables (Saldias, 2017).
3.2. Data
We use the monthly data from 2008M2
to 2018M2 on Gap, FSI, CPI, and policy
interest rate.
Among these variables- output gap(GAP)
represent for economic growth and
economic output, as the monthly data of
GDP is hard to measure, the study use
industrial production index (IIP) to replace
for GDP to account for economic growth.
GAP is calculated through the change of
industrial production index(IIP)and HP
filter in Eviews software.
In this study, we use financial stress
index data for Vietnam calculated by the
Asian Development Bank (ADB), which
is based on Park and Mercado (2014)
methodology. According to ADB (2019),
the FSI for the Vietnamese economy
is computed using measures for four
major financial sectors with the equation
presented as follows:
FSI = β + Stockreturns + Stockvolatility +
Debtspreads + EMPI
The five FSI components in the equation
come from five sectors and variables :
banking Sector withincluding banking
sector price index and stock price index;
equity market returns including the
current period’s equity return and its lag;
equity market volatility; debt markets
with sovereign debt spreads(=long-term
Table 1. Variables and source
Variables Source
Financial stress index (FSI) Asian Development Bank website (www.adb.org)
Consumer price index (CPI) General Statistics Office of Vietnam website (www.gso.gov.vn)
Output gap( GAP) General Statistics Office of Vietnam website (www.gso.gov.vn)
Policy interest rate (INT) State bank of Vietnam website (www.sbv.org)
ANH THI LAM PHAM
Số 217- Tháng 6. 2020- Tạp chí Khoa học & Đào tạo Ngân hàng 79
(10-year) local government bonds- US
Treasuries in basis points) ; foreign
exchange market with the exchange
market pressure index (EMPI).
All series are seasonally adjusted by using
X-12 method and then taken in natural
logarithm (except for the policy interest
rate) before estimation. We also conduct
the unit root test by using the Augmented
Dickey-Fuller (ADF) test and Phillips-
Perron (PP) test (Table 2). The results
suggest that all series are stationary at first
difference. After that, we set up the VAR
estimation in the first difference.
4. Empirical results and discussion
4.1. Estimation of the inflation threshold
In this section, we employ the method of
Tsay (1998) to decide the financial stress
threshold for Vietnam. Our objective is
separating to the high and low financial
stress regime using distinct sets of model
parameters. Based on the value of the finan-
cial threshold, the times series can be split
into two different cases. When the financial
stress threshold variable is higher than the
critical value, the time points are classified
as the high regime. Otherwise, the time
points are classified as a low regime.
Table 3 indicates the results of test statistic
C (d) rejecting the null hypothesis of the
linear relationship in all cases of different
starting numbers for recursive estimates
(m0 = 30 and m0 = 50). This implies
that FSI is a suitable threshold variable,
and that is worthwhile to split into two
regimes. We decide the threshold lag
delay (d) is 1, corresponding to delay
by a month. For two-regime models, we
assume the threshold γ ϵ [-4, .05] and use
300 grid points. The interval determination
is based on the value of the threshold
variable. The estimated threshold value for
the output gap is 0.35, with the smallest
AIC of (-143.00724).
The low regime is active when the FSI
is below the estimated threshold, 0.35.
It presents the standard period of the
economy, which is described by the
economic growth and low financial market
stress. In converse, the high regime
indicates the economy moves to the
slowdown situation characterized by high
financial market stress.
Figure 1 shows a plot of the estimated
output threshold value and the threshold
Table 2. Unit root test
Variables ADF test PP test
P-value Conclusion P-Value Conclusion
GAP 0.0000 stationary 0.0001 Stationary
FSI 0.094 Non stationary 0.0000 Stationary
FSI at 1st difference 0.0000 Stationary 0.0000 Stationary
CPI 0.0000 stationary 0.0001 Stationary
Interest_ rate 0.0992 Non-stationary 0.2362 Non-stationary
Interest_ rate at st difference 0.0001 stationary 0.0000 Stationary
Source: Author’s computation in Eview 10
The effect of financial stress index on the Vietnamese economic growth - A threshold auto
regression approach
Tạp chí Khoa học & Đào tạo Ngân hàng- Số 217- Tháng 6. 202080
variable. The estimated threshold value di-
vides our sample into two regimes that are
highly consistent with the economic devel-
opment in Vietnam. The high episode
dominates the period from 2008 to 2009.
During this time, the Vietnam economy
experienced a decline in economic activi-
ties, the struggle of the banking system,
and the reduction in the stock market. The
low episode is captured by some period
from 2010 to 2015 and the time after
2016. At that time, the economic grew and
stabled; the reconstruction of the banking
system has some progress. The empirical
model endogenously selects the separation
of the sample.
4.2. Impulse response analysis
After splitting the sample into the high and
low regime, we estimate the VAR model
in each regime. We assume two lags in
both cases. Figure 2 to 4 indicates the
estimated impulse response functions over
12 months horizon in linear VAR, high
regime, and low regime.
In the case of the line VAR, FSI is the
unique variable that effects on GAP,
although this influence is not significant.
CPI and IR do not show their impact on
the growth of the industrial production
index. On contrast, GAP is seen to have
an impact on CPI and IR.
In the high regime, FSI is shown to have a
positive effect on GAP while GAP harms
FSI, but this is not significant. GAP has
a positive response to the increase in CPI,
but CPI is not seen to not react to GAP.
In this episode, GAP will increase in the
short term with the positive IR shock
then decrease, but IR is understood to not
respond to the GAP shock.
In the low regime (Figure 4), GAP will go
Table 3. Result of the threshold test
FSI threshold
d C(d) p-value
1 30 57.36 0.013
1 50 66.76 0.001
γ 0.35 AIC -143.00724
Source: Author’s computation in RAT pro 8.0
*Note: d is a delay for the threshold variable, is
starting number for recursive estimates, C(d) tests
statistic based on the method of Tsay (1998). AIC is
the Akaike Information Criterion
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
-6
-4
-2
0
2
4
6
Figure 1. FSI
Source: Author’s computation in RAT pro 8.0
Note: The solid line illustrates FSI, the dotted line indicates the threshold value (0.35), and the shaded area
is the high FSI period.
ANH THI LAM PHAM
Số 217- Tháng 6. 2020- Tạp chí Khoa học & Đào tạo Ngân hàng 81
up when it faces to positive IR and CPI
shock, but GAP is seen to have no impact on
both these variables. GAP almost has no-
reaction to the FSI shock in this scenario.
For linear VAR, high regime, and low
regime case, GAP is seen to have a
response to all other variables in senior
regime while the reaction of GAP to other
shocks in linear VAR and low regime case
is quite the same. The positive response of
GAP to the positive trauma of IR and CPI
in all scenario shows that monetary policy
seems to have a litter effect on output.
4.3. Discussion
In general, the impacts of shocks in the
Linear VAR
R
es
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gap
cpi
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1.4
Figure 2. Impulse response functions in linear VAR
Source: Author’s computation in RAT pro 8.0
Note: The impulse responses (mid-solid line) are presented over a 12-month period along the horizontal
axis. 68% confidence intervals based on Monte Carlo simulation are plotted around each response (as per
Sims and Zha, 1995)
High
R
es
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es
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cpi
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int
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-0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0 1 2 3 4 5 6 7 8 9 10 11
-2
-1
0
1
2
3
4
5
0 1 2 3 4 5 6 7 8 9 10 11
-2
-1
0
1
2
3
4
5
0 1 2 3 4 5 6 7 8 9 10 11
-2
-1
0
1
2
3
4
5
0 1 2 3 4 5 6 7 8 9 10 11
-2
-1
0
1
2
3
4
5
0 1 2 3 4 5 6 7 8 9 10 11
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 1 2 3 4 5 6 7 8 9 10 11
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 1 2 3 4 5 6 7 8 9 10 11
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 1 2 3 4 5 6 7 8 9 10 11
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 1 2 3 4 5 6 7 8 9 10 11
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
0 1 2 3 4 5 6 7 8 9 10 11
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
0 1 2 3 4 5 6 7 8 9 10 11
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
0 1 2 3 4 5 6 7 8 9 10 11
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
Figure 3. Impulse response functions in high FSI regime
Source: Author’s computation in RAT pro 8.0
Note: The impulse responses (mid-solid line) are presented over a 12-month period along the horizontal
axis. 68% confidence intervals based on Monte Carlo simulation are plotted around each response (as per
Sims and Zha, 1995)
The effect of financial stress index on the Vietnamese economic growth - A threshold auto
regression approach
Tạp chí Khoa học & Đào tạo Ngân hàng- Số 217- Tháng 6. 202082
two regimes are quite different. There is
no doubt that the FSI threshold strongly
affects the relationship among GAP, CPI,
FSI, and interest rate under various states
of the economy. However, the effect of
FSI on the GAP in the case of Vietnam
does not follow the economic theory,
when in the high regime, the positive
shock FIS would lead to the increase
in GAP (economic growth). Although
the reaction of GAP to FIS shock is not
significant, the sign of reaction is still
unusual, compared to previous studies.
This adverse result can be explained by
the following reasons.
The first reason comes from financial
stress data. The study used the ADB
financial stress index data based on Park
and Mercado (2014) methodology. For
this FIS methodology, Park and Mercado’s
(2014) estimated the instability in the
banking sector through β = cov(r,m)/
var(m). In this case, r and m are the returns
to the banking stock price index and the
overall stock price index, respectively.
The higher the banking sector β, the
higher, the greater the banking sector
stress. The advantage of this estimation
is easy to collect the data of banking
sector equity from the Vietnamese stock
market database. However, the drawback
of this measurement is that the number
of banking equities in Vietnamese stock
market in the period of 2008-2014 only
accounted for small part in the number
of bank in Vietnam. Thus, β might not
represent fully the risk and instability
in the banking sector as well as in the
financial market in Vietnam.
The second reason of the unusual the
relationship between financial stress and
economic growth may come from the
lag choice of model. The study chose
the lag for the model is only two, while
Goktas and Hepsag (2011) indicated that
the transmission of the stock market to
economic activity would last within six
months. Thus, two-month lags would not
be enough time for the financial stress to
transmit its negative impact to economic
output and economic growth. However,
6 to 12 months lags may cause the model
Low
R
es
po
ns
es
o
f
gap
cpi
dfsi
int
gap
gap
cpi
cpi
dfsi
dfsi
int
int
0 1 2 3 4 5 6 7 8 9 10 11
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0 1 2 3 4 5 6 7 8 9 10 11
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0 1 2 3 4 5 6 7 8 9 10 11
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0 1 2 3 4 5 6 7 8 9 10 11
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0 1 2 3 4 5 6 7 8 9 10 11
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 1 2 3 4 5 6 7 8 9 10 11
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 1 2 3 4 5 6 7 8 9 10 11
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 1 2 3 4 5 6 7 8 9 10 11
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 1 2 3 4 5 6 7 8 9 10 11
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50
0 1 2 3 4 5 6 7 8 9 10 11
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50
0 1 2 3 4 5 6 7 8 9 10 11
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50
0 1 2 3 4 5 6 7 8 9 10 11
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50
0 1 2 3 4 5 6 7 8 9 10 11
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 1 2 3 4 5 6 7 8 9 10 11
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 1 2 3 4 5 6 7 8 9 10 11
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0 1 2 3 4 5 6 7 8 9 10 11
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Figure 4. Impulse response functions in low FSI regime
Source: Author’s computation in RAT pro 8.0
Note: The impulse responses (mid-solid line) are presented over 12 months along the horizontal axis. 68%
confidence intervals based on Monte Carlo simulation are plotted around each response (as per Sims and
Zha, 1995)
ANH THI LAM PHAM
Số 217- Tháng 6. 2020- Tạp chí Khoa học & Đào tạo Ngân hàng 83
to be unable to estimate the threshold.
Therefore, the longer data for financial
stress index is essential to access the effect
of financial stress index on economic
growth in Vietnam.
These limitations may be a suggestion
for further researches to develop a new
method to calculate the volatility in the
banking sector, as well as the new method
to estimate financial stress for the bank-
base economies.
5. Conclusion
This paper extends the literature analyzing
the impact of financial stress on economic
activity. Earlier studies demonstrated that
financial stress had a negative influence on
the output of industrial in both advanced
and developing economies. However, in
the case of Vietnam, whether financial
stress has an impact on economic
activities has not been studied. Employing
the threshold vector auto-regression,
the paper finds that there exist financial
stress index threshold which divides the
Vietnamese financial stress index into two
regimes: high regime. In the low regime,
FSI has no influence on GAP measured
by industrial production index (represent
for economic activity) while in the high
regime, the positive shock in FSI would
follow by the increase in GAP. Although,
this effect is not significant, the reaction
of economic activity to the change in FSI
does not follow the economic theory. This
usual result might source from the method
that is used to estimate FSI and the
limitation of data. The study’s limitations
suggest that researchers and policy-makers
develop new methods to compute financial
stress and conduct more research on the
impact of financial stress on economic
growth and other economic variables in
Vietnam. The results of these studies will
help Vietnamese policy-makers formulate
macro-prudential policy for a financial
system more comprehensively ■
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trong tương lai, các doanh nghiệp hiện
tại cần tăng cường các biện pháp nhằm
đảm bảo khả năng tạo tiền, kiểm soát chặt
chẽ mối quan hệ cân đối giữa sử dụng
tiền và nguồn tiền, chủ động lập kế hoạch
dòng tiền làm nền tảng tích cực cho việc
khai thác nội lực và hiệu quả khi sử dụng
các nguồn tài trợ từ bên ngoài nhằm tạo
nên khoản thặng dư tiền trong kỳ, qua
đó tăng số dư tiền nắm giữ. Ngoài ra,
kết quả nghiên cứu còn gợi ý rằng các
doanh nghiệp cần kiểm soát chặt chẽ quy
mô doanh nghiệp, duy trì quy mô doanh
nghiệp phù hợp với khả năng kiểm soát rủi
tiếp theo trang 73
ro của bản thân doanh nghiệp; đồng thời
các doanh nghiệp cần tăng cường học tập
và tích lũy kinh nghiệm quản lý tài chính
cũng như quản lý rủi ro kiệt quệ tài chính
từ các doanh nghiệp có thâm niên hơn ■
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