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JEFAS
27,53 Impact of financial stress
in advanced and
emerging economies
68 Flavio C
esar Valerio Roncagliolo
Department of Economics, Universidad de Lima, Lima, Peru, and
Received 16 May 2021
Revised 15 July 2021 Ricardo Norberto Villamonte Blas
Accepted 6 September 2021 Department of Economics, Universidad Nacional Mayor de San Marcos, Lima, Peru

Abstract
Purpose – The purpose of the paper is to examine the differences in the impact of financial stress in advanced
and emerging economies.
Design/methodology/approach – The authors employ a panel vector autoregression model (PVAR) for a
comparative analysis of the relationship between financial stress, economic growth and monetary stability in
14 advanced and emerging economies. A homogeneous measure of financial stress is constructed and
measured as an index that provides signals of stress episodes in an economy.
Findings – The impact of financial stress shocks is greater on the economic growth of advanced economies;
likewise, financial stress shocks are significant only in advanced economies. The interbank interest rate is
negatively affected by financial stress in emerging economies. In general, the results show a clear view of the
importance of financial stability and the economic relevance of financial stress measures in the context of
macro-prudential regulation.
Originality/value – The results can be extended to monetary policy to implement measures that mitigate the
impact of future financial crises.
Keywords Economic growth, Inflation, Interest rate, Real estate market, Financial stress, PVAR
Paper type Research paper

1. Introduction
The stability of the financial system became pertinent during the integration of the financial
system and its subsequent deregulation in the 1990s (Stiglitz, 2003). The worldwide financial
crisis of 2008–2009 generated great economic instability and, with it, fluctuations in the
growth of the major economies, which led to a reduction in the production of imports and
exports. Very few economies did not report reductions in their Gross Domestic Product (GDP)
at that time. The most noticeable consequence of this financial crisis was the collapse of
Lehman Brothers, the fourth-largest investment bank in the USA. This initiated the collapse
of the entire American financial system (Verick and Islam, 2010). Following this event, a lot of
research has been conducted to analyse the relationship between the banking system,
financial stress and economic growth. Brunnermeier (2009), Adrian et al. (2010) and Davig
and Hakkio (2010) conducted studies of the USA; Dhal et al. (2011) conducted studies of India;
Van Roye (2011) focused on Germany and Mallick and Sousa (2013) and Apostolakis et al.
(2019) analysed the impact of financial stress on European economies; however, to the best of
our knowledge, this is the first study to conduct a comparative analysis of the impact of
financial stress between advanced and emerging economies.

© Flavio C
esar Valerio Roncagliolo and Ricardo Norberto Villamonte Blas. Published in Journal of
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 According to Smets (2014), maintaining financial stability helps ensure a well-functioning Differences in
financial system, which generates efficient price stability. Likewise, macro-prudential policies the impact of
can help reduce financial stress index (FSI) as they correctly manage the financial cycle, which
increases the resilience of the financial sector to possible economic instability. Financial stress
financial stress
is a disruption in the functioning of financial markets as an incoherent intermediary between
borrowers, lenders, buyers and sellers (Sandahl et al., 2011). Studying financial stress ensures
that if systemic financial stress levels can be detected at an early stage, fiscal and monetary
policy measures can be taken to mitigate the potential impact on the economy (Haefcket and 69
Skarholt, 2011). Claessens and Habermeier (2013) indicate that monetary policy aims to
maintain price stability. They also point out that macro-prudential policies focus on the search
for financial stability. Similarly, Blot et al. (2020) argue that an unconventional monetary policy
hurts sovereign yields, thus reducing the level of financial stress in the economy.
We also examined the relationship between the stock market, public debt and financial
stress. Cipollini and Mikaliunaite (2020) argue that there is a transmission effect of financial
stress in highly integrated markets as in the case of the Eurozone or the Latin American
Integrated Market (MILA). They also posit that the level of debt has a determining effect on
the transmission capacity. Jaramillo et al. (2017) argue that the level of debt affects financial
markets regardless of the state of the economy, effectively reducing the returns of the stock
market and increasing the yields on sovereign bonds.
We posit that this research will improve the understanding of the impact of financial
stress on economic activity, as well as the channels through which financial stress increases.
In addition, the index could serve as a precautionary measure against possible crises in the
financial market and consequently informing measures to mitigate the impact of financial
stresses on economies. This study analyses two models: the first seeks to find the relationship
between GDP, inflation and financial stress in a sample of emerging and advanced economies,
and the second seeks to find the relationship between the variables of the first model, the
interest rate and house prices. Our hypothesis is as follows: Financial stress affects the
economic growth, financial stability and monetary stability of advanced economies more than
emerging economies. To prove this hypothesis, we employed panel vector autoregressive
(PVAR) model techniques to investigate the correlations in financial data obtained from
seven advanced economies (The USA, Canada, Germany, France, Spain, Italy and
Switzerland) and seven emerging economies (Peru, Chile, Colombia, Mexico, Brazil, China
and Russia) from the year 2005 to the second quarter of the year 2019. The econometric
estimations show that financial stress hurts all the variables analysed more significantly in
advanced economies. Our hypothesis was thus proven.

2. Literature review
2.1 Financial stress index
Financial stress is popularly defined as the periods in which economic agents are exposed to
extreme uncertainties leading to negative expectations of financial markets (Schinasi, 2004;
Allen and Wood, 2006). Illing and Liu (2006) created a financial stress index (FSI) which was
applied to Canada by incorporating continuous variables where extreme values corresponded
to periods of crisis. Likewise, Cardarelli et al. (2011), Balakrishnan et al. (2011) and Park and
Mercado (2014) used variants of this method and applied it to emerging economies.
Van Roye (2011) constructed a financial stress measure for Germany using several bank
measures, including the US Treasury Bond and the Eurodollar. Haefcket and Skarholt (2011)
constructed a daily financial stress measure for Sweden by dividing the Swedish financial
market into 5 parts and incorporating their individual measures into a single index, whilst
Park and Mercado (2014) constructed a homogeneous measure of financial stress for 25
emerging economies.
JEFAS Dhal et al. (2011) constructed a banking stability index to study the relationship between
27,53 financial stability and economic growth in India. They found that economic growth, inflation
and financial market stability share a medium-to-long-term relationship. They also found
that financial stability can help economic growth and improve the effectiveness of monetary
policies. Mallick and Sousa (2013) examined the impact of financial stress and monetary
policy on the economy. They found that monetary policy shocks cause an increase in interest
rate, inflation, financial stress, GDP and loan growth in the early periods following the shock.
70 Financial stress shocks can also lead to a decrease in commodity prices, GDP, interest rates
and an increase in economic growth.
Malega (2015) constructed a FSI with a specific focus on the Czech Republic, finding a
significant and positive response to unemployment due to the impact of financial stress.
They also found that financial stress hurts inflation and interest rates. Tng (2017)
constructed a FSI for Malaysia, the Philippines and Thailand, concluding that higher-
financial stress leads to tighter domestic credit conditions and lower-economic activity in all
five economies, and the impact on the real economy shows a rapid initial decline followed by
a gradual dissipation.
Creel et al. (2015), Stolbov and Shchepeleva (2016) and Landgren and Crook (2018)
analysed the impact of financial stress on various economies, finding that financial stress
hurts economic activities such as industrial production or inflation; furthermore, they
highlighted the importance of macro-prudential monitoring of financial stability and the
importance of central banks and policymakers in implementing tools for this purpose. Polat
and Ozkan (2019) examined the impact of the financial stress in the Turkish economy; they
found that the deterioration of the financial system reduces industrial production and
increases inflation. They argued that stress levels should be monitored as an unconventional
tool by policymakers to avoid financial instability. Ozcelebi (2020) examined the relationship
between financial stresses in advanced economies with the dynamics of the exchange market
in emerging economies. They concluded that high levels of financial stress can depreciate the
currencies of emerging economies; this effect on the exchange rate will have a positive effect
on the volatility of the stock market in the short term (Mroua and Trabelsi, 2020). Likewise,
Eldomiaty et al. (2020) examine the relationship between the stock market sector, inflation
and the interest rate using a cointegration model, concluding that there is a negative
relationship of inflation with stock market performance, and the interest rate has a positive
relationship, which is also indicating that stock market stability requires interest rate
stability and robust control on inflation.
This study is related to Cardarelli et al. (2011) and Park and Mercado (2014), who employed
a measure of financial stress to examine the cross-border transmission of financial stress.
Balakrishnan et al. (2011) investigated how financial stress is transmitted from advanced to
emerging economies. They found that the degree of financial stress is linked to the depth of
financial relationships between advanced and emerging economies. Similarly, Hung (2019)
conducted a study of the returns and volatility spillover of financial markets, finding that
financial markets became even more integrated during the crisis, generating a volatility
spillover effect that has relevant implications for policymakers. This study is also related to
Apostolakis and Papadopoulos (2015) who analysed the impact of financial stress on
economic growth, inflation and real estate for advanced economies in the Organisation for
economic Co-operation and Development (OECD) through Panel vector autoregression
(VAR). However, we extend the literature to include an analysis of the impact of financial
stress in advanced and emerging economies.

2.2 Construction of the financial stress index

The construction of the FSI follows the work of Balakrishnan et al. (2011) and Park and
Mercado (2014), who constructed a homogeneous FSI for advanced and emerging economies.
 Banking sector. Due to the large variety of economies chosen for the sample, the Differences in
heterogeneity of the sample and available data, the measure of banking stress “banking the impact of
sector beta” as in Balakrishnan et al. (2011) was used. This banking sector beta follows
Sharpe’s (1964) Capital Asset Pricing Model (CAPM) model and is given by as follows:
financial stress
covðr; mÞ
β¼
varðmÞ
71
where r and m are the bank stock and market returns, respectively. The volatility of the
banking sector is measured depending on the value of β; the higher the value of β, the higher
the level of stress in which the banking sector is.
For the construction of the beta, we used monthly data on the average of the returns of the
banks listed on each stock exchange of the economies in the sample and the return of the main
stock market index of each country. The treatment of the data follows the work of Park and
Mercado (2014): (1) the data were converted into year-to-year returns by taking the difference
in logarithms of the current period’s price with concerning last year, (2) the β was calculated
using the covariance and variance of the returns in 12 months and (3) the series were
transformed to take a positive value if they exceeded the value 1 and 0 otherwise.
Foreign exchange market. This study uses the conditional volatility of the monthly change
in the nominal effective exchange rate quantified by a Generalized AutoRegressive
Conditional Heteroskedasticity GARCH (1, 1) process. This measure is a weighted basket of
the nominal exchange rate concerning the foreign currencies of a given country.
Stock market. Two stock market measures are included in the composition of the FSI. The
first is stock market returns multiplied by minus one, where a decrease in returns will cause
the index to increase (Apostolakis et al., 2019). The second component is stock market
volatility, which is measured as a GARCH (1,1) process considering the distribution that best
fits the characteristics of stock market returns. The GARCH (1,1) process proposed by
Bollerslev (1986) and Engle (2001) is composed of two equations as follows:
pffiffiffiffi
ri ¼ μi þ hi εi ; ∀i εf1; . . . ; N g (1)
hi ¼ ω þ αðri1  μi1 Þ þ βhi−1
2
(2)

where Equation (1) is the equation of the returns of the main stock indexes of each country, μi is
the mean of r for each i and εi is the error term for each i that follows a normal distribution
N ∼ (0,1). Equation (2) specifies the conditional variance process for each hi that depends on the
quadratic term of ðri−1 − μi−1 Þ and the variance hi−1. This equation only makes sense if ω > 0,
α > 0, β > 0 and α þ β < 1 are satisfied (Bollerslev, 1986; Engle, 2001). This method for measuring
stock market volatility and incorporating it into a stress index was used by Balakrishnan et al.
(2011), Cardarelli et al. (2011) and Park and Mercado (2014). Monthly data were used, and the
returns were calculated using the month-to-month difference in stock market indices.
Debt market: In this research, due to the limited availability of data on emerging
economies, we chose to use the measure adopted by Cardarelli et al. (2011) research, which
uses the yield spreads of the ten-year sovereign bond with respect to the US Treasury bond; in
the USA, only the yield of the treasury bond will be used. This measure not only captures the
systemic risk of the sovereign debt market but also any form of fiscal fragility (Rodr
ıguez-
Moreno and Pe~ na, 2013).

2.3 Financial stress index weighting scheme

The choice of the weighting method of the financial stress components in a single index is of
great importance in this construction. The method mostly used for the construction of this
index is equality of variances in which the FSI is constructed by assigning each of the
JEFAS components similar importance and assuming that the series are normally distributed and
27,53 that each series is degraded and standardised. Furthermore, equality of variances is suitable
for the construction of indexes that measure the severity of financial stress in a heterogeneous
sample of economies (Das et al., 2005). Each component was calculated as follows:
ðxt  xÞ
yt ¼
σ
72
where yt is the degraded and standardised series of each component, x is the mean of the time
series and σ is the standard deviation of the series. The degraded and standardised
components are then transformed into values from 0 to 1, where 1 is the largest historical
value. The choice was based on the research by Cardarelli et al. (2011), Yiu et al. (2010), Park
and Mercado (2014) and Apostolakis et al. (2019).
Finally, the FSI will be composed as follows:
FSIi;t ¼ βi;t þ Stock Market Returni;t þ Stock Market Volatilityi;t þ Debt Spreadi;t
þ Exchange Market Volatilityi;t

where a FSI greater than zero will mean episodes of financial stress in the economies in the
sample and a value less than zero will mean that the financial markets are stable.

3. Method
3.1 Data collection
Quarterly economic growth (GDP), consumer price index (CPI), house prices (HP), short-term
interest rate and FSIs’ data were obtained from Bloomberg and the Bank for International
Settlements (BIS). To obtain more balanced panel data and achieve consistency in findings,
monthly data were converted to quarterly data. The data ranges from Q1 2005 to Q2 2019;
2019 full-year data were not considered due to unavailable data sources for both economies at
the time this research took place. The sample is divided into two groups: emerging economies
and advanced economies.
Table 1 shows the descriptive statistics of the sample. As can be seen, there is a higher
variability of financial stress in advanced economies; furthermore, financial stress has a
negative correlation with house prices and economic growth and a positive correlation with
interest rate and inflation. To test the stationarity of the series, unit root tests for panel data
were carried out; the tests used were Harris and Tzavalis (1999), Hadri (2000), Breitung (2005),
Phillips and Perron (1988), Levin et al. (2002) and Im et al. (2003). These many tests were
necessary to check for unit roots in the panel data series. The results reject the null hypothesis
of the presence of unit roots in the panel data series at a 5% level of significance, except for the
interest rate; therefore, this variable will be estimated in differences.

3.2 Econometric estimation

We employed the PVAR model to investigate the relationship between the impact of financial
stress on economic growth, and monetary and financial stability. The VAR methodology
allows for the treatment of the study variables as endogenous, thus enabling us to examine
the effect of the shocks of one variable on the other. Following the model of Love and Zicchino
(2006), we used the PVAR model together with the generalised method of moments (GMM) to
examine and compare the impact of stress between advanced and emerging economies. The
PVAR model was formulated as follows:
Yi:;t ¼ G0 þ GðLÞYit−1 þ fi þ dt þ et
 Advanced economies panel Emerging economies panel
GDP CPI HP Interest FSI GDP CPI HP Interest FSI

Mean 1.40 1.45 0.28 0.98 0.00 4.18 4.48 0.89 5.89 0.00
Median 1.78 1.48 0.40 0.36 0.43 4.06 3.89 0.79 4.58 0.27
Minimum 7.17 1.62 6.75 1.87 4.46 9.4 3.03 26.12 0.43 4.65
Maximum 5.93 5.30 5.61 5.26 9.89 15.00 16.2 13.22 19.67 8.91
Std. dev. 2.08 1.19 1.73 1.51 2.39 3.68 2.86 3.15 3.56 2.31
Skewness 1.41 0.12 0.52 1.06 0.8 0.12 1.42 0.99 1.29 0.76
Kurtosis 2.55 0.06 1.56 0.14 0.65 0.98 3.07 15.03 1.28 0.71
Sample size 406 406 406 406 406 406 406 406 406 406

Pairwise correlation coefficients

GDP CPI HP Interest FSI GDP CPI HP Interest FSI

GDP 1 1
CPI 0.1760*** 1 0.300*** 1
HP 0.3917*** 0.185*** 1 0.2583*** 0.135*** 1
*** *** * ***
Interest 0.1628 0.5850 0.029 1 0.298 0.6865*** 0.010* 1
*** ** *** ** *** ***
FSI 0.481 0.0745 0.359 0.1023 1 0.165 0.2488 0.127*** 0.2073*** 1
*** ** *
Note(s): Correlation coefficients were calculated using Pearson’s correlation test. , and denote the level of significance at 1, 5 and 10%, respectively
Source(s): Own elaboration
financial stress

73
the impact of
Differences in

Table 1.
Descriptive statistics
JEFAS where Yi:;t is a vector of endogenous variables; G0 is the vector of constants; GðLÞ is the matrix
27,53 of polynomial lag operators; fi is the fixed effects parameter that captures time-invariant
effects unobservable at the country level; dt is the forward mean difference parameter and et is
the vector of independent and identically distributed errors. The time series were
disaggregated and forward skewed using the Helmert procedure as in Apostolakis et al.
(2019). This was done since the fixed effects are correlated with the regressors (Arellano and
Bover, 1995); likewise, the models were estimated by employing GMM-style instrumental
74 variables as proposed by Holtz-Eakin et al. (1988).
In the following section, first, the results of the simple model and the extended five-
variable model are presented; second, the Granger causality tests for each equation of the
PVAR model are presented; third, the impulse response functions (IRFs) are presented. For
greater accuracy of the confidence intervals, 1,000 Monte Carlo (MC) iterations are used, and
finally, the forecast error variance decomposition (FEVD) is analysed.

4. Results
In the VAR models, the variables are arranged according to the Cholesky decomposition,
which assembles the variables introduced in the model from the most exogenous to the most
endogenous, followed by the introduction of macro-economic variables in the system. The
models proposed for both advanced and emerging economies are as follows:
Base model: GDP  CPI  FSI
Extended model: GDP  CPI  Real estate prices  Interest rate  FSI

This ordering is since the shocks originate from the real sector of the economy and the
financial sector is affected by several such shocks.

4.1 Base model

Base model estimation results. Table 2 shows the results of the PVAR (1) model for emerging
and advanced economies. As can be seen, the impact of financial stress on the economy is
much more significant in advanced economies on the first lag. Meanwhile, the effect of the
financial stress shock on price stability, which is measured as inflation, is not significant in
any of the economies.
Granger causality of the base model. Table 3 shows the Granger causality results of both
models. The null hypothesis of this test is: FSI does not Granger-cause GDP or inflation. As

Emerging economies Advanced economies

***
GDP L. GDP 0.9179 GDP L. GDP 0.8804***
L. CPI 0.1302*** L. CPI 0.2126***
L. FSI 0.0690** L. FSI 0.0740***
CPI L. GDP 0.0853*** CPI L. GDP 0.0535***
L. CPI 0.9360*** L. CPI 0.9057***
L. FSI 0.03020 L. FSI 0.0001
FSI L. GDP 0.0459** FSI L. GDP 0.0420
L. CPI 0.1792*** L. CPI 0.3538***
***
L. FSI 0.7461 L. FSI 0.7851***
Note(s): The PVAR model was estimated with one lag according to the modified Bayesian information
criterion (MBIC). No. of observations: 812 per variable; no. of panels: 14 panels. ***, ** and * denote significance
Table 2. level at 1 5 and 10%, respectively
Base model coefficients Source(s): Own elaboration
Emerging economies Advanced economies
Differences in
Equation/excluded Chi square Equation/excluded Chi square the impact of
financial stress
GDP GDP
CPI 7.897*** CPI 27.156***
**
FSI 2.597 FSI 9.606***
Todas 7.985*** Todas 44.868***
CPI CPI 75
GDP 31.430*** GDP 11.101***
FSI 1.447 FSI 0.004
Todas 33.368*** Todas 12.380***
FSI FSI
GDP 2.837** GDP 1.504
***
CPI 12.602 CPI 24.960***
Todas 13.189*** Todas 33.402***
Note(s): These results are based on a three-variable PVAR model (1). Values are Wald’s chi-square statistics. Table 3.
*** **
, and * denote the significance level at 1, 5 and 10%, respectively Granger causality of
Source(s): Own elaboration the base model

can be seen, Wald statistics in the case of emerging economies indicate bidirectional causality
between financial stress and economic growth, i.e. FSI has a causal effect on GDP and vice
versa. In the case of advanced economies, there is unidirectional causality between FSI and
GDP, and financial stress has a causal relationship with economic growth.
Impulse response functions. Figures 1 and 2 show the IRFs for emerging and advanced
economies, respectively. They also show that in the case of emerging and advanced
economies, GDP responds negatively and significantly to a financial stress shock, being more
significant in the latter. These results are consistent with previous research (Mallick and
Sousa, 2013; Creel et al., 2015; Apostolakis and Papadopoulus, 2015). In the case of FSI, it
responds positively to shocks to CPI and economic growth in emerging economies; in the case
of advanced economies, FSI responds positively to shocks to GDP and CPI. Inflation responds
significantly and positively after one lag to a GDP shock in advanced and emerging
economies. GDP responds negatively to CPI shocks in advanced and emerging economies.
These results are also consistent with Mallik and Chowdhury (2001).
Forecast error variance decomposition (FEVD) of the base model. Table 4 shows the results
of the FEVD of the PVAR base model (1), which shows that FSI in the case of advanced
economies is responsible for 7% and 1.5% of the variations in GDP and CPI, respectively. For
emerging economies, FSI is responsible for 1% and an average of 2% of the changes in GDP
and CPI. Meanwhile, in the case of macroeconomic variables, GDP is responsible for 20% and
15% of the variations in FSI and CPI and 21% and 20% in emerging and advanced
economies, respectively. Also, the results show that FSI is more responsible for GDP
variations in emerging economies than advanced economies. However, in advanced
economies, FSI is more responsible for CPI variations.

4.2 Extended model

Extended model estimation results. Table 5 shows the estimation results of the extended
model PVAR (1); as can be seen, FSI continues to hurt GDP in both emerging and advanced
economies but has only a positive and significant impact on CPI in advanced economies. Also,
it can be seen that FSI hurts real estate prices in both emerging and advanced economies but
only hurts interest rates in emerging economies. This finding can be attributed to European
economies reducing their interest rates to negative levels from 2014 (regardless of the level of
stress in their economies) as a measure for price stability.
JEFAS
27,53

76

Figure 1.
IRFs of the three-
variable model for
emerging economies

Granger causality of the extended model. The results in Table 6 show that most variables
can predict each other, i.e. they are bidirectional. In the case of emerging economies, FSI has a
bidirectional relationship with house prices and a unidirectional relationship with the interest
rate and the CPI; however, in advanced economies, FSI has a bidirectional relationship with
the CPI and house prices and a unidirectional relationship with the interest rate.
Impulse response functions of the extended model. Figures 3 and 4 show the IRFs for the
emerging and advanced economies, respectively. The IRFs show that FSI shocks have similar
effects on real estate prices in both emerging and advanced economies but are more
significant in advanced economies. The other variables of the model do not present
significant IRFs in the presence of FSI shocks. Meanwhile, the FSI is positively affected by
shocks to all model variables in both emerging and advanced economies.
Variance decomposition (FEVD) of the extended model. Table 7 shows the results of the
FEVD of the extended PVAR model (1). As can be seen, FSI and inflation are responsible for
the greater proportions of the variations of GDP in emerging economies and advanced
economies, respectively. Concerning the new aggregate variables, FSI is more responsible for
the changes in real estate prices in advanced economies than in emerging economies, and the
opposite is true for the interest rate. Meanwhile, GDP, inflation and real estate prices are more
responsible for the changes in FSI in advanced economies than in emerging ones.

5. Discussion
Most of the results obtained in this study are consistent with previous studies. For example,
Apostolakis and Papadopoulus (2015) states that financial stability should be monitored
 Differences in
the impact of
financial stress

77

Figure 2.
IRFs of the three-
variable model for
emerging economies

Emerging economies Advanced economies

Impulse variable Impulse variable
Response variable/forecast horizon GDP CPI FSI GDP CPI FSI

GDP
10 0.8611 0.1373 0.0016 0.5221 0.3952 0.0827
20 0.7634 0.2300 0.0063 0.4501 0.4846 0.0652
GDP
10 0.2310 0.7537 0.0152 0.2896 0.7018 0.0085
20 0.3693 0.6158 0.0150 0.3071 0.6683 0.0246
GDP
10 0.0848 0.2123 0.7029 0.1017 0.2053 0.6929 Table 4.
20 0.2015 0.1958 0.6027 0.1585 0.2082 0.6333 FEVD of the
Source(s): Own elaboration base model

macro-prudentially through tools developed by central banks and policymakers. Our results
consistently indicate a significant increase in inflation from a financial stress shock.
Similarly, they also indicate that financial stress shocks hurt economic growth, government
deficit and house prices. Park and Mercado (2014) state that the FSI has a great impact on
economies and that a shock in the stress index can spread to other economies. Further, they
posit that the financial stress of emerging economies is affected by global growth and other
similar factors. Consistently, our results also show that the stress index of advanced and
emerging economies affects various domestic economies; global and domestic factors also
JEFAS Emerging economies Advanced economies
27,53
GDP L. GDP 0.9442*** GDP L. GDP 0.5947***
L. CPI 0.0409 L. CPI 0.3077***
L. House prices 0.0126 L. House prices 0.0829***
L. Interest rate 1.2551*** L. Interest rate 3.7689***
L. FSI 0.0109* L. FSI 0.0342*
78 CPI L. GDP 0.0399*** CPI L. GDP 0.0278*
L. CPI 0.8756*** L. CPI 0.8794***
L. House prices 0.0171 L. House prices 0.0194
L. Interest rate 0.7362*** L. Interest rate 1.0726***
L. FSI 0.0257 L. FSI 0.0298***
House prices L. GDP 0.2617*** House prices L. GDP 0.0234
L. CPI 0.1951*** L. CPI 0.0803
L. House prices 0.2612*** L. House prices 0.5677***
L. Interest rate 1.2610*** L. Interest rate 0.1907
L. FSI 0.1035** L. FSI 0.1300***
Interest rate L. GDP 0.0137**
Interest rate L. GDP 0.0289***
L. CPI 0.0353*** L. CPI 0.0302***
L. House prices 0.0103* L. House prices 0.0281***
L. Interest rate 0.6000*** L. Interest rate 0.9953***
L. FSI 0.0195** L. FSI 0.0071
FSI L. GDP 0.0432** FSI L. GDP 0.0310
L. CPI 0.1141*** L. CPI 0.3519***
L. House prices 0.0540*** L. House prices 0.1130***
L. Interest rate 0.1749*** L. Interest rate 0.6231***
Table 5.
Extended model L. FSI 0.7628*** L. FSI 0.8295***
results for emerging Note(s): The PVAR model was estimated with one lag according to the MBIC. No. of observations: 812 per
and advanced variable; no. of panels: 14 panels. ***, ** and * denote significance level at 1, 5 and 10%, respectively
economies Source(s): Own elaboration

affect the stress index, and trade openness has a positive effect on financial stress. Mallick
and Sousa (2013) posit that monetary policy shocks cause an increase in the interest rate,
inflation, financial stress, GDP and loan growth in the first period following the shock.
According to them, financial stress shocks lead to a decrease in commodity prices, GDP,
interest rates and an increase in money growth. Finally, they hold that monetary policy
shocks lead to an increase in the interest rate and a decrease in money growth. Similarly, our
results also show that financial stress shocks lead to a decrease in economic growth and an
increase in inflation.
Furthermore, Dhal et al. (2011) indicated that a bank stability index shock causes an
increase in inflation and economic growth but a decrease in interest rates. In turn, an interest
rate shock causes an increase in inflation and a decrease in economic growth. Consistently, in
our results, the third model shows that inflation and economic growth shocks cause an
increase in the interest rate. Cevik et al. (2013) constructed FSIs for five economies and
examined their relationship with economic activities. Their results show that in most
economies, a financial stress shock prolongs the industrial production index, as well as the
other indicators of economic activity taken into consideration in their model. Further, they
found that a shock of the industrial production index decreases the financial stress in that
economy. Although the variables studied are not the same as in our case, they demonstrate
the same impact that occurs in a scenario of a positive shock of the FSI to other exogenous
variables. Finally, Kırcı Çevik et al. (2019) examined the relationship between inflation and the
FSI using a Markov regime-switching model. The results show that the monetary policy of
the economies analysed is consistent with the Taylor rule. They found that the low-inflation-
Emerging economies Advanced economies
Differences in
Equation/excluded Chi square Equation/excluded Chi square the impact of
financial stress
GDP GDP
CPI 1.330 CPI 37.909***
House prices 0.330 House prices 6.522***
Interest rate 95.835*** Interest rate 174.682***
FSI 0.124 FSI 2.941* 79
Todas 99.839*** Todas 204.775***
CPI CPI
GDP 8.553*** GDP 3.533*
House prices 1.635 House prices 1.298
Interest rate 214.924*** Interest rate 142.185***
FSI 2.374 FSI 7.126***
Todas 347.583*** Todas 204.190***
House prices House prices
GDP 28.093*** GDP 0.439
***
CPI 13.565 CPI 2.398
Interest rate 89.166*** Interest rate 1.342
FSI 4.900*** FSI 20.627***
Todas 97.854*** Todas 57.831***
Interest rate Interest rate
GDP 4.678** GDP 20.687***
***
CPI 9.640 CPI 5.604**
House prices 3.548* House prices 12.723***
FSI 4.046*** FSI 2.197
Todas 30.298*** Todas 58.864***
FSI FSI
GDP 3.505* GDP 1.151
***
CPI 10.633 CPI 46.887***
***
House prices 7.893 House prices 11.631***
***
Interest rate 5.421 Interest rate 11.756***
Todas 24.535*** Todas 106.115***
Note(s): These results are based on a 5-variable PVAR model (1). Values are Wald’s chi-square statistics. ***, ** Table 6.
and * denote the significance level at 1, 5 and 10%, respectively Granger causality of
Source(s): Own elaboration the extended model

targeting regime is more persistent and has a longer duration than the high-inflation-
targeting regime. They provide evidence that financial stress has a statistically significant
impact on monetary policy in the studied economies. Although they used a different model,
our results are consistent with theirs.

6. Conclusions
We examined the differences in the impact of financial stress on economic growth, financial
stability and monetary stability between advanced and emerging economies.
We found that financial stress had an impact on all the variables analysed, being more
significant in advanced economies. We thus posit that financial stress affects economic
growth, financial stability and monetary stability more in advanced economies.
We found a negative relationship between the stress index and economic growth, with the
impact of the stress index being much greater in advanced economies. Notably, our results
are consistent with most studies in the literature; however, they are more pronounced, given
the methodology used for the composition of the FSI (that was calculated homogeneously for
the 14 economies using the variables available in all of them.)
JEFAS
27,53

80

Figure 3.
IRFs of the extended
model for emerging
economies
 Differences in
the impact of
financial stress

81

Figure 4.
IRFs of the extended
model for advanced
economies
JEFAS Response Emerging economies Advanced economies
27,53 variable/ Impulse variable Impulse variable
forecast
horizon GDP CPI HP Interest FSI GDP CPI HP Interest FSI

GDP
10 0.5009 0.0287 0.0076 0.4553 0.0074 0.3266 0.0587 0.0276 0.5853 0.0018
82 20 0.5026 0.0273 0.0071 0.4529 0.0100 0.3124 0.0621 0.0253 0.5919 0.0083
CPI
10 0.1175 0.6414 0.0062 0.2039 0.0309 0.2463 0.2098 0.0132 0.5022 0.0258
20 0.1590 0.5811 0.0066 0.2223 0.0307 0.2306 0.2002 0.0135 0.5146 0.0410
HP
10 0.1072 0.0183 0.7164 0.1535 0.0044 0.0441 0.1365 0.6637 0.0437 0.1120
20 0.1304 0.0187 0.6831 0.1634 0.0043 0.0911 0.1406 0.4681 0.1846 0.1153
Interest
10 0.0185 0.1072 0.0073 0.8561 0.0109 0.2343 0.0188 0.1297 0.7271 0.0068
20 0.0203 0.1085 0.0073 0.8522 0.1172 0.2352 0.0187 0.0142 0.7249 0.0069
Table 7. FSI
Variance 10 0.0940 0.0970 0.0140 0.0180 0.7770 0.1443 0.2210 0.0039 0.1920 0.4309
decomposition (FEVD) 20 0.1655 0.0892 0.1251 0.0564 0.6763 0.1691 0.1677 0.0090 0.3751 0.2790
of the extended model Source(s): Own elaboration

Inflation had a positive relationship with financial stress and a statistically significant effect
on the economic activity of advanced economies. This can be attributed to the high amount of
imported goods in these economies, which in high-financial stress scenarios (where the local
currency depreciates) increases the local price basket.
Another important point to highlight is the relationship between the interest rate and
financial stress, which shows mixed results; there is a negative relationship in the case of
emerging economies, which is consistent with past research where a reduction in the interest
rate occurs in scenarios of the financial crisis due to the reduction in consumption. In the case
of advanced economies, we found that there is no relationship between financial stress and
the interest rate. However, this can be attributed to the composition of our sample (being
mostly European economies where the existing recession made them apply negative interest
rate policies to reactivate the financial system regardless of the level of financial stress in the
economy).
Overall, our results show that high levels of advanced economies’ indebtedness to banks
generate insolvency and high levels of public debt, which, in turn, increase the impact of
financial stress on these economies. Meanwhile, for the emerging economies analysed, due to
the low fiscal deficits that allow for greater penetration of their monetary policies to mitigate
episodes of financial stress, the effect of financial stress shocks to them is diminished.
Therefore, the FSI should be adapted to each economic sector and monitored by central banks
to avoid liquidity problems, and monetary policy should focus on the export sector in cases of
external transmission of financial stress, increasing money demand and external reserves.
Furthermore, the results obtained in this research are quite relevant for economies with
high-trade openness, whose markets are exposed to the economic, financial and political
problems of other economies (because financial markets are becoming increasingly
integrated). Likewise, it is possible to analyse the impact of financial tensions in different
economic sectors and apply appropriate measures to mitigate the impact of future economic
crises.
Finally, the FSI constructed here can be incorporated as a non-conventional policy tool of
economic relevance in the context of macro-prudential regulation, so central banks and
policymakers can develop supervisory frameworks to examine financial stability and
soundness. To reduce financial stress in their economies, central banks or monetary Differences in
authorities should prioritise the stability of their banking sectors and the solvency of their the impact of
financial sectors.
financial stress
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Corresponding author
Flavio C
esar Valerio Roncagliolo can be contacted at: 20132323@aloe.ulima.edu.pe

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