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Is financial institutions’ stability of BRICS block

responsive to uncertain dimensions?

Rehan Aftab & Muhammad Naveed |

To cite this article: Rehan Aftab & Muhammad Naveed | (2022) Is financial institutions’ stability of
BRICS block responsive to uncertain dimensions?, Cogent Business & Management, 9:1, 2010484,
DOI: 10.1080/23311975.2021.2010484

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Aftab & Naveed, Cogent Business & Management (2022), 9: 2010484
https://doi.org/10.1080/23311975.2021.2010484

BANKING & FINANCE | RESEARCH ARTICLE

Is financial institutions’ stability of BRICS block
responsive to uncertain dimensions?
Rehan Aftab1* and Muhammad Naveed2

Received 29 May 2021

Abstract: The aim of this seminal paper is the empirical analysis of geopolitical risk,
Accepted 20 November 2021 economic policy uncertainty, financial stress, and infectious diseases’ impact on
*Corresponding author: Rehan Aftab, financial institutions’ stability at the country level. The quantitative research
PhD Scholar, Faculty of Management approach followed by regression analysis is employed using monthly time series
Sciences, Szabist, H-8, Islamabad,
Pakistan data between January 2000 and January 2021. Separate models are performed for
E-mail: rehan.aftab@nu.edu.pk; dr.
naveed@szabist-isb.edu.pk
individual countries with and without control variables. The outcomes of this work
suggest that geopolitical risk, economic policy uncertainty, financial stress, and
Reviewing editor:
David McMillan, University of Stirling, infectious diseases hold adverse effects on the financial institutions’ stability. There
Stirling, United Kingdom
is evidence of declining financial institutions’ stability with the rising level of pre­
Additional information is available at dicting dimensions. The research is limited to the BRICS block but has paramount
the end of the article
significance for literature enrichment and policymakers. The findings can assist
decision-makers to plan for uncertain events disrupting the financial system. More
rigorous research techniques can be levered to endorse the consistency of the
evidence. The dimensions adopted in this study address the ongoing paradigm of
research. The financial institutions’ stability at the country level is a value addition
of this work with the selection of persistent and novel predictors of financial
systems like financial stress and infectious diseases.

Subjects: Corporate Finance; Banking; Credit & Credit Institutions

Keywords: Geopolitical risk; economic policy uncertainty; financial stress; infectious

diseases; financial institutions stability

ABOUT THE AUTHOR PUBLIC INTEREST STATEMENT

We belong to the same institute but have dif­ This study has far stretched implications for dif­
ferent roles to play in academic research. We ferent stakeholders who are affected by distrac­
are actively participating in the research studies tions in the financial and economic environment.
directed to examine the modalities of the The uncertain happenings can change the way of
financial system and financial markets. This thinking and these can hamper the individual
work is a fine instance of our interest to look at capability to make the right decision. The general
the financial system with an agile approach. We public as the key stakeholder of the issue of
are looking into varying changes in the financial “financial stability” must understand that their
markets as part of our research activities. interests are linked to the economic environment
they are living in. Factors like rising financial stress
and contagion diseases generate harmful conse­
quences for financial systems and the general
public is the ultimate victim of this disruption. So,
the public must educate itself on the latent
dynamics of the financial and economic
landscape.

© 2021 The Author(s). This open access article is distributed under a Creative Commons
Attribution (CC-BY) 4.0 license.

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1. Introduction
The dynamic nature of the economic and financial world undergoes consistent changes due to
uncertain events in the external environment. The economic theorists suggest that uncertain
situations are of paramount significance for economic agents while making decisions in the
financial world (Borch, 2015). Extending this notion, this paper has its basis for examining the
financial system and the uncertain happenings bringing changes therein. Historically, several
events and economic shocks of uncertain nature have devastated the normal course of financial
institutions as the key component of the financial system (Sabaté, 2016). The stability of financial
institutions becomes questionable with the changing paradigms of the external world. Financial
stability in terms of Čihák and Hesse (2010) is the smooth functioning of the financial system
where funds management and financial intermediation are efficient, and the financial system can
absorb external shocks.

Financial markets as one component of the financial system are rigorously analyzed for their
volatility in the presence of numerous dimensions like geopolitical risk, economic policy uncer­
tainty, financial stress, and other systemic shocks like the Global Financial Crisis (BenSaïda et al.,
2018; Kannadhasan & Das, 2020). Recently, the health uncertainty as Covid-19 has also distracted
the financial systems around the globe and the aftermaths of this ongoing pandemic are eminent
from financial downturns (Villarreal, 2020). These distractions are also harmful to small and
medium-scale enterprises (Aftab et al., 2021). So, there emerges a need for scholars to investigate
the effects that different sorts of uncontrollable events and persisted events have on the normal
course of the financial system.

The analysis of the rising level of uncertainties and financial markets as constituents of the
financial system is evident from empirical evidence from different strands of economies (Apergis
et al., 2018; Gozgor et al., 2016; Zhang et al., 2019). These authors worked out geopolitical risk,
economic policy uncertainty, financial stress, and pandemic in the perspective of financial market
volatility. The financial system has two pillars for funds flow and management usually recognized
as financial markets and financial institutions. Financial institutions also have a role to play in the
financial outlook along with financial markets (Berndsen et al., 2018). So, there is a need for
analyzing the financial institutions’ stability to ascertain the financial outlook in presence of
numerous factors including geopolitical risk, economic policy uncertainty, financial stress, and
infectious diseases.

Jones et al. (2012) linked an uncertain environment with the institutional-level stability in the
United States. Gilchrist et al. (2014) related uncertainties with the financial disruptions in numer­
ous countries. Moreover, the drivers of financial institutions’ stability are rigorously analyzed in the
existing body of knowledge at sector and firm-specific levels. A recent work of Phan et al. (2021)
has empirically analyzed the effect of economic policy uncertainty on financial stability driven by
financial institutions of 23 countries. It only used the sample of developed countries and provided
the basis to extend the effort with more rigor to different economic blocks. So, the emerging
nations BRICS block, financial institutions’ stability is under review of this paper.

The financial institutions’ stability in literature is analyzed in the context of the banking sector.
The existing empirical evidence concludes enormous drivers of financial stability in any country on
the sector and individual institution levels. Global factors disrupting the financial system through
impact on the financial market also need analysis in terms of impact on financial institutions
within the system. Phan et al. (2021) study in recent times provides the basis for establishing the
link between global-level factors and country-level financial institutions’ stability.

So, this paper aims to empirically examine the impact of a few global drivers of the financial
system. How geopolitical risk, economic policy uncertainty, financial stress, and infectious diseases
affect financial institutions’ stability? The financial institutions’ stability is taken on the country
level for BRICS block. It represents a set of economies at an advanced stage of development

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chasing the developed nations. Due to the interlinked financial systems of the world, the global-
level factors seem to have the same implications for all linked countries.

This paper holds paramount significance for scholars and policymakers. The analysis of financial
institutions concerning a few global drivers of change enriches the financial institutions and
markets literature. The policymakers can have a look at all those factors that can globally interfere
in their institutions and are mostly uncontrollable. Contingency planning becomes easier with such
established relationships in the literature. Risk management frameworks can be established by
financial institutions with due regard to these uncertain elements. Future studies can develop
more econometrics techniques to address the underlying research question. The sector-specific
and firm-specific controls can be employed to have a deep insight into the financial institutions’
stability. The work should be extended to other developing countries as well.

2. Literature review
The theories of standard finance have an indirect link with the level of uncertainty and financial
institutions’ stability. Macroeconomic factors like inflation have their role to play in the ascertain­
ment of financial soundness in the country. The uncertain events influence the macroeconomic
factors, and this effect is carried over to the financial system. So, the traditional theories like CAPM
and DDM have an indirect link with the financial institution’s stability and its drivers. Whereas the
economic theory creates a direct link of the global uncertain drivers with the level of financial
institutions’ stability in the country. Economic agents make their decisions in the agile and
uncertain world hence leading to distorted happenings in the financial system (Borch, 2015). So,
this theoretical perspective is carried out throughout this work. The activities of economic agents
trigger changes in the financial system and these agents are influenced by few known global
factors having a ripple down effect on financial markets. These factors as reviewed in this work are
geopolitical risk, economic policy uncertainty, financial stress, and infectious diseases.

Financial system stability is primarily dependent on the stability of its financial institutions and
the resilience nature of financial institutions defines the notion of financial institutions’ stability
(Berndsen et al., 2018). The monetary aspect of the financial system and financial stability are two
different aspects but are associated with each other, so the external events influence both. So,
both monetary and financial institutions’ stability policymakers work together on integrated
policies (Phan et al., 2021; Smets, 2018). The uncertain external environment affects the financial
system of the countries due to financial integration. An uncertain event in one country may affect
the financial system of another country, this spillover effect is systemic Gilchrist et al. (2014). These
uncertainties bring financial distortion and decline the overall stability of the financial systems.

The dimensions examined in this paper include geopolitical risk, economic policy uncertainty,
financial stress, and infectious diseases. Geopolitical risk develops with the level of tensions and
conflicts between the states. The geopolitical risk during wars is on the higher side (Lee & Wang,
2021). Secondly, it is the economic policy uncertainty, which is the unpredictability of government
policies and decisions. Stakeholders of an economic system are when unaware of government
decisions, this spikes the economic policy uncertainty level (Al-Thaqeb & Algharabali, 2019).
Thirdly, it is financial stress representing the unfavorable movement of financial variables leading
to an uncertain situation for economic users (Aboura & van Roye, 2017). Lastly, infectious diseases
as another dimension in this paper include the several outbreaks of the 21st century. This dimen­
sion captures the events of disease outbreaks as epidemics and pandemics as declared by World
Health Organization (Villarreal, 2020).

Likewise, the financial stress that is pressure on the financial variables of the economies also
harms the financial system and its constituents (Apostolakis & Papadopoulos, 2015). The regres­
sion analysis based on the OLS regression process identified the impact of financial stress on the
financial institution’s stability. The rising level of financial stress carries financial instability from
one financial constituent to another. It narrates that the financial institutions’ stability is not apart

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from the financial stability of financial markets. So, both constituents of the financial system have
to take effect of financial stress levels (Apostolakis & Papadopoulos, 2015). Lee et al. (2017)
analyzed financial institutions’ stability as a function of bank-specific factors and economic policy
uncertainty. They concluded that the bank-specific factors and the level of economic policy
uncertainty hold a significant impact on the overall financial institutions’ stability. They followed
the regression model for estimation and concluded that the bank-specific factors and economic
policy uncertainty have a significant and negative impact on the financial institutions’ stability.
Caglayan and Xu (2019) studied financial institutions’ stability in response to the economic policy
uncertainty. Banking-level stability was the concerning area in the work of Caglayan and Xu (2019),
who found that the level of economic policy uncertainty significantly and negatively impacts the
stability of banks in the sector. Loan loss provisions and non-performing loans were constituents
taken from the banking sector for analysis of its stability.

Baum et al. (2018) extended the analysis of financial institutions’ stability to numerous uncer­
tain events in the market. The uncertain situations in the external financial world hold
a significant impact on the financial institution’s stability. The financial stability and growth
depend on the financial stress level of advanced economies. Apostolakis and Papadopoulos
(2019) studied the sample of 19 advanced economies using the autoregressive model for the
possible impact of financial stress on the financial institutions’ stability and growth. They com­
pleted the work in a dynamic context with the application of a dynamic estimation model. The
findings suggest a negative and significant impact of financial stress on the financial institutions’
stability and growth. Phan et al. (2021) examined the financial institutions’ stability of 23 different
developed countries in response to changing economic policy uncertainty. The financial institu­
tions’ stability was measured using a z-score that scales the institutions’ probability to default.
The work concluded that a rising level of economic policy uncertainty declines the level of
financial institutions’ stability of 23 developed countries on the country level. Baig et al. (2021)
analyzed the financial institutions’ stability in times of the recent pandemic. The study found that
pandemic has negatively affected the financial institutions’ stability of the United States. They
also found similar effects of a pandemic for the United States equity markets. Regression was
used to estimate the coefficients explaining the impact of a pandemic on the financial institu­
tions’ stability. The model proved significant to analyze the impact of a pandemic on the financial
institutions’ stability. The recent work of Phan et al. (2021) analyzed the impact of economic policy
uncertainty with the control factors of the macro-economic environment on financial institutions’
stability. The authors concluded that the stability-level shifts with the changes in economic policy
uncertainty.

Aysan et al. (2019) using autoregressive modeling analyzed the efficacy of geopolitical risk in
predicting financial market volatility. The geopolitical risk proved a significant predictor of financial
market volatility. Gkillas et al. (2020) analyzed commodity market behavior in response to geopo­
litical risk and they concluded the adverse and significant effect of geopolitical risk on commodity
prices. In the context of geopolitical risk, not much has been evaluated for country-level financial
institutions’ stability. The reviewed empirical evidence related to geopolitical risk and financial
system is mostly concerned with one constituent of the financial system that is financial markets.
This derives the need for extending the analysis in the context of geopolitical risk relationship with
the soundness of financial institutions. So, this notion is extended in the paper.

Infectious diseases historically have been a concerning dimension for financial systems due to
their ripple effect. Many infectious emergencies got the attention of international researchers and
their associated financial significance was examined (Kilgo et al., 2018). In times of recent pan­
demic, researchers started to examine both constituents of the financial system, financial markets,
and financial institutions, for the possible impact of contagion disease. Bouri et al. (2020); Bai et al.
(2020) attempted to rigorously examine the financial markets’ response to the health crisis. The oil
indices and stock indices are examined in their work and found to have a negative effect on the
health crisis. Dynamic models were employed by these authors to examine the impact of the crisis

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on the oil indices and stock indices. VAR is the basic model used for estimations and finding the
significant outcomes for researchers.

Economic watchdogs and experts of the financial system viewed the current situation as
disastrous for the financial system. It can have spillover effects, which can carry to other financial
systems from origination (Li et al., 2020). After reviewing a few of the dominant studies after the
sudden surge of the pandemic, the financial institutions’ stability seems an underexamined area of
research. The existing literature has focused on financial markets as a single constituent of the
financial system. Financial institutions need more empirical examination to identify the accurate
effect of a pandemic on its viability. So, in response to infectious disease, financial institutions’
stability is under the question of this work.

The review of relevant literature and empirical evidence from the different economies, financial
systems endorse financial institutions’ stability as the function of numerous uncertainties in the
external world. Economic policy uncertainty is the most studied dimension having an impact on
financial institutions’ stability. In recent times, health emergency has also emerged as a driver of
financial institutions’ stability. The least work has been evaluated in terms of uncertainties influencing
the normal course of financial institutions. The dimensions of this work as geopolitical risk, economic
policy uncertainty, financial stress, and infectious diseases have relevance with the financial system
as their occurrences have an impact on financial institutions’ stability. Financial institution stability in
literature is mostly evaluated on banking-level dimensions, and the analysis of financial institution
stability on a country level is almost the missing link in the literature. So, having new dimensions of
uncertainties, this work also has the evaluation of financial stability on the country level.

3. Methodology

3.1. Research design

The scope of this paper confines analyzing the impact of geopolitical risk, economic policy uncer­
tainty, financial stress, and infectious diseases on financial institutions’ stability. In line with the
aim of the paper, the quantitative research approach better fits with the regression model as an
econometric technique for statistical analysis. The time-series data for all variables are extracted
from secondary sources from January 2000 till January 2021. With this period, the data frequency
is monthly for all dimensions and is selected for bringing uniformity in observations of variables at
the modeling phase. The population pertinent to the research scope includes all countries with
developed financial systems but the time and resource constraints have confined the final sample
to a selection of BRICS countries.

3.2. Dependent and independent variables data and measures

Geopolitical risk is the outcome of growing tensions between the countries in a specific region or
on the global fronts. The geopolitical risk is measured using an established index that is the
Geopolitical Risk Index (GPRI), it is an effort of Dario Caldara and Matteo Lacoviello. Its data for
the stated time series (January 2000—January 2021) is sourced from Matteo Iacoviello
Geopolitical Risk Index Database. This index considers geopolitical tensions from various regions
for its construction and has an aspect of cross-border events.

For economic policy uncertainty, Baker, Bloom, and Davis global economic policy uncertainty
index is adopted for its measurement (Davis, 2016). It describes the economic policy uncertainty
due to unpredictable decisions of governments in 21 different countries. It provides monthly
statistics for economic policy uncertainty as required in this work.

Likewise, the financial stress index approved and constructed by the Office of Financial Research
is taken for the measurement of financial stress in this paper. Its efficacy for the empirical analysis
is endorsed by Ozcelebi (2020). It measures the stress level of financial variables across emerging,
developed, and developing economies.

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Similarly, infectious diseases as predictors are measured using Baker et al. (2020) equity market
volatility tracker. It is an index based on numerous health uncertainties. Its monthly frequency is
obtained using reporting of health-related keywords from published sources to construct this index.

Lastly, the measurement of financial institutions’ stability is based on the capital adequacy of
the whole financial system at the country level. Capital adequacy as a proxy measure of financial
institutions’ stability across different countries of the World is part of the World Bank list of
financial soundness indicators. The World Bank database provides monthly reporting of financial
institutions’ capital adequacy aimed at measuring the overall financial soundness of the under­
lying financial systems.

3.3. Control variables data and measures

Financial institutions’ stability can get an impact of macroeconomic variables as reported by Phan
et al. (2021). So, inflation rate (INF), the growth rate of GDP (GDP), and gross domestic product per
capita (GDPC) are taken as control variables in the modeling. These controls ensure the robustness
of outcomes in the modeling process. Ramasamy and Abar (2015) also endorsed the use of
macroeconomic variables while examining financial stability. The monthly data for control vari­
ables is extracted for the stated time series (January 2000—January 2021) from FRED economic
data and the World Bank database.

3.4. Econometric model

The regression equations are developed below for analyzing the impact of geopolitical risk,
economic policy uncertainty, financial stress, and infectious diseases on financial institutions’
stability. Equation 1 only includes the impact of independent variables on the dependent variable.
Whereas equation 2 includes the impact on control variables analyzing the robustness of the
modeling. The same equations are separately performed for different countries included in the
BRICS block. OLS regression is performed for analysis as it is more reliable when it comes to time
series data with stationarity at level or first difference. The same OLS regression model is used in
the work of (Phan et al., 2021). The basic estimation equations are presented below:

CAði;tÞ ¼ α þ βðGPRIÞi;t þ βðGEPUIÞi;t þ βðFSIÞi;t þ βðEMVÞi;t þ ε1 (Equation 1)

CAði;tÞ ¼ α þ βðGPRIÞi;t þ βðGEPUIÞi;t þ βðFSIÞi;t þ βðEMVÞi;t þ βðGDPPCÞi;t

þ βðΔGDPÞi;t þ βðΔCPIÞi;t þ ε1 (Equation 2)

In the above equations, the CA represents capital adequacy as a measure of financial institutions’
stability (i for a specific country and t for a specific time). GPRI is the measuring index of
geopolitical risk. GEPUI is the measuring index of economic policy uncertainty. FSI is the financial
stress index measuring financial stress. EMV is the infectious diseases market volatility tracker
index as a measure of infectious diseases. GDPPC is the country’s gross domestic product per
capita. ΔGDP is the percentage change in GDP measuring the growth rate of GDP. ΔCPI is a change
in the consumer price index measuring the inflation rate.

4. Data analysis
The preliminary analysis constitutes correlation analysis and descriptive analysis. Later, the ana­
lysis of econometric models is performed. At first, Table 1,2 has the correlation values for all
independent variables in the econometric model.

The correlation values of all independent variables are significant and the relationship between
the variables is not much strengthening, which may have led to a weak theoretical and econo­
metric model. Following the correlation analysis, it is the descriptive analysis of variables in table 3.

The total number of observations for all variables in the chosen time series is equivalent to 253.
It is a balanced figure for all variables. The other important statistic is the unit root testing using

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Table 1. Has basic details related to variables under discussion

Variables/Frequency Measure Source
Geopolitical Risk Geopolitical Risk Index (GPRI) Matteo Iacoviello Geopolitical Risk
Index Database.
Economic Policy Uncertainty Global Economic Policy Baker, Bloom, and Davis Global
Uncertainty Index (GEPUI) Economic Policy Uncertainty Index.
Financial Stress Financial Stress Index (FSI) Office of Financial Research (FSI).
Infectious Diseases Daily Infectious Disease Equity Daily Infectious Disease Equity
Market Volatility Tracker (EMV) Market Volatility Tracker Baker
et al. (2020).
Financial Institutions Stability Capital Adequacy (CA) Financial Soundness Indicator
FRED Economic Data—World Bank
Data.
Gross Domestic Product Per Capita Country’s GDP per capita FRED Economic Data—World Bank
(GDPC) Data.
Growth Rate of GDP (GDP) Percent changes in GDP FRED Economic Data—World Bank
Data.
Inflation Rate (INF) Change in the consumer price FRED Economic Data—World Bank
index Data.

the ADF test. The null hypothesis that there is a unit root is significantly rejected as the p values for
all variables are significant below 1% level of significance. The time series is stationary and is
suitable for analysis as depicted by the ADF test. With this analysis, the main empirical model
analysis is shown in Table 4.

Dependent variable: Capital Adequacy, first row for coefficients, second row with {} for p-values,
third row with () denotes t-statistic.

The regression analysis in table 4 shows 10 different models performed using 2 basic equations.
Equation 1 is used to perform model 1 to model 5. Each time capital adequacy as a dependent
variable is changed for changing countries. From model 6 through model 10, the basic equation 2
is separately performed for each country included in the BRICS block.

In column 1, the capital adequacy of Brazilian financial institutions’ stability is the dependent
variable. Geopolitical risk, economic policy uncertainty, financial stress and equity market volatility
tracker for infectious diseases are having negative and significant impact on the Brazilian financial
institutions’ stability (GR = −0.039, p = 0.012, EPU = −0.047, p = 0.001, FS = −0.004, p = 0.035,
EMV = −0.023, p = 0.011). The independent variables explain the 72% variation in the Brazilian financial
institutions’ stability. While in column 6 control variables are included with the same set of indepen­
dent variables for Brazil. The impact of independent variables is not significantly and directionally
changed, whereas control variables have their nature of impacts on the financial institution’s stability.

In column 2, Russian financial institutions’ stability is measured while taking the geopolitical risk,
economic policy uncertainty, financial stress, and equity market volatility tracker for infectious
diseases as predictors. The independent variables bring 70% variability in the capital adequacy and
the impact is negatively significant as well (GR = −0.022, p = 0.000, EPU = −0.027, p = 0.003,
FS = −0.050, p = 0.000, EMV = −0.092, p = 0.000). This infers that the rising level of uncertainty due
to independent variables brings a decrease in the financial institution’s stability of Russia. Whereas
in column 7 control variables are included for Russia to check robustness with the same set of
independent variables. Even in this model, the impact of independent variables is not significantly
and directionally changed. While the control variables have their nature of impacts on the financial
institution’s stability.

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Table 2. Correlation analysis

GPRI GEPUI FSI EMV GDPPC ΔGDP ΔCPI
GPRI 1
GEPUI 0.286** 1
FSI −0.396* 0.426** 1
EMV −0.226* 0.597** 0.146* 1
GDPPC 0.440 0.002* 0.505 0.429** 1
ΔGDP −0.047** 0.329 0.127** 0.218* 0.154 1
ΔCPI −0.621 0.591** −0.457 0.482 0.381* 0.284* 1
*Denotes statistical significance at 1 %, ** denotes statistical significance at 5 %, * denotes statistical significance at
10 %.

Table 3. Descriptive summary

Obs. Mean S.D ADF Sig
GPRI 253 105.77 71.36 −4.71 0.000
GEPUI 253 133.62 69.34 −3.44 0.000
FS 253 6.68 93.68 −3.52 0.000
EMV 253 50.25 168.5 −6.32 0.000
GDPPC 253 2.01 0.94 −3.49 0.000
ΔGDP 253 0.12 0.04 −4.18 0.000
ΔCPI 253 0.15 0.06 −4.26 0.000
CA (Brazil) 253 9.19 4.22 −3.75 0.000
CA (Russia) 253 10.37 5.9 −4.9 0.000
CA (India) 253 11.84 4.87 −3.54 0.000
CA (China) 253 12.64 3.44 −3.21 0.000
CA (SA) 253 10.27 4.01 −3.61 0.000

In column 3, the geopolitical risk, economic policy uncertainty, financial stress and equity market
volatility tracker for infectious diseases represents negative and significant impact on the financial
institutions’ stability of India (GR = −0.001, p = 0.005, EPU = −0.078, p = 0.006, FS = −0.062,
p = 0.005, EMV = −0.034, p = 0.000). These variables explain an overall 79% variability in the
financial institutions’ stability of India. Likewise, in column 8 control variables are included for
India with the same set of independent variables to examine the capital adequacy of the country.
While controlling for control variables, the independent variables still have a significant and
directional impact.

In the 4th column, the IVs explain about 81% variation in the financial institutions’ stability
of China. The geopolitical risk, economic policy uncertainty, financial stress and equity
market volatility tracker for infectious diseases shows negative and significant impact on
the financial institutions’ stability of China (GR = −0.017, p = 0.021, EPU = −0.018, p = 0.003,
FS = −0.093, p = 0.005, EMV = −0.060, p = 0.000). Model 9 represents the impact of
geopolitical risk, economic policy uncertainty, financial stress, and equity market volatility
tracker for infectious diseases on Chinese financial institutions’ stability with controlled
macroeconomic variables. The outcomes for independent variables are alike to the model
without controls with minute differences. Control variables have their varying nature of
influence.

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 Table 4. Regression (country-wise analysis)
1 2 3 4 5 6 7 8 9 10
https://doi.org/10.1080/23311975.2021.2010484

Brazil Russia India China South Af. Brazil Russia India China South Af.
GPRI −0.039 −0.022 −0.001 −0.017 −0.084 −0.009 −0.019 −0.092 −0.099 −0.816
{0.012} {0.000} {0.005} {0.021} {0.061} {0.043} {0.000} 0.000 {0.002} 0.059
(−2.019) (−3.939) (−2.582) (−2.007) (−1.899) −1.999 -(3.719) −3.326 −3.124 (−1.900)
Aftab & Naveed, Cogent Business & Management (2022), 9: 2010484

GEPUI −0.047 −0.027 −0.078 −0.018 −0.010 −0.081 −0.079 −0.048 −0.010 −0.062
{0.001} {0.003} (0.006} {0.003} {0.095} {0.000} {0.002} {0.003} {0.004} {0.060}
(−3.193) (−3.001) (−2.528) (−2.878) (−1.655) (−3.991) (−2.918) (−2.901) (−2.628) (−1.863)
FSI −0.004 −0.050 −0.062 −0.093 −0.018 −0.019 −0.068 −0.059 −0.041 −0.015
{0.035} {0.000} {0.005} {0.005} {0.000} {0.020} {0.000} {0.030} {0.003} {0.005}
(−2.114) (−3.833) (−2.509) (−1.991) (−3.376) (−2.001) (−3.489) (−2.210) (−2.836) (−2.575)
EMV −0.023 −0.092 −0.034 −0.060 −0.047 −0.015 −0.079 −0.028 −0.057 −0.026
{0.011} {0.000} {0.000} {0.000} {0.000} {0.010} {0.004} {0.030} {0.000} {0.000}
(−2.169) (−3.626) (−3.539) (−3.301) (−3.507) (−2.296) (−2.721) (−2.033) (−3.301) (−3.567)
GDPPC 0.223 0.143 0.258 0.002 0.190
{0.343} {0.305} {0.438} {0.152} {0.301}
(0.438) (0.899) (0.245) (1.437) (0.620)
ΔGDP 0.349 0.061 0.073 0.021 0.201
{0.193} {0.021} {0.171} {0.020} {0.004}
(1.267) (1.001) (1.399) (2.001) (1.887)
ΔCPI 0.052 0.067 0.049 0.011 0.015
{0.393} {0.362} {0.137} {0.191} {0.202}
(0.340) (0.579) (1.102) (0.995) (0.839)
Adj. R2 0.729 0.701 0.799 0.81 0.691 0.736 0.713 0.804 0.819 0.703

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Lastly, in column 5 it is the financial institutions’ stability of South Africa in response to

geopolitical risk, economic policy uncertainty, financial stress, and equity market volatility tracker
for infectious diseases. These variables explain 69% variation in the capital adequacy of South
Africa and also hold negative and significant impact on it (GR = −0.084, p = 0.061, EPU = −0.010,
p = 0.095, FS = −0.018, p = 0.000, EMV = −0.047, p = 0.000). The last column shows geopolitical risk,
economic policy uncertainty, financial stress, and equity market volatility tracker for infectious
diseases impact on financial institutions’ stability of South Africa with macroeconomic control
variables. The findings for independent variables are consistent with the findings of model 5. South
African financial stability seems significant at a 10% confidence interval while estimating in
response to geopolitical risk and global economic policy uncertainty. This might be attributed to
the fact that global policy uncertainty does not count for data coming from South Africa in its
construction. Among 21 countries, all other members are included except South Africa. The higher
confidence interval of geopolitical risk impact on financial stability may be attributed to lack of
tensions in the region of its destination. The unrest and tensions are mostly out of its region and
specific to other blocks.

5. Discussion
The data analysis results in several findings from this empirical work. The basic relationship
between the uncertainties and financial stability as governed by the economic theory is substan­
tiated by the evidence of this work (Borch, 2015). Geopolitical risk, economic policy uncertainty,
financial stress, and infectious diseases as uncertain dimensions significantly affects the financial
institutions’ stability as suggested by the findings of this work. A similar sort of finding is referred to
from the review of empirical evidence in the existing body of knowledge. Gilchrist et al. (2014)
concluded the significant effect of different global level uncertain dimensions on different econo­
mies. The same significant effect of geopolitical risk, economic policy uncertainty, financial stress,
and infectious diseases is observed on financial stability for each country in the BRICS block as
evident from this paper.

Geopolitical risk has negative consequences for financial stability as concluded by Gkillas et al.
(2020). In this study, geopolitical also has a negative impact on financial institutions’ stability.
Aysan et al. (2019) found geopolitical risk as a driver of volatility in the financial system and the
same sort of implications are evident from this work. The geopolitical tensions as measured by the
risk index covering different regions spikes in the event of conflicts and wars. When there is conflict
or tension between different countries, the risk index shows spikes and at the same time, financial
system stability is declined. This is how geopolitical risk impacts financial institutions’ stability.

Economic policy uncertainty and financial stress have empirically adverse effects on the sound­
ness of the financial system (Phan et al., 2021; Smets, 2018) while analyzing financial institutions’
stability as a constituent of the entire financial system, the same sort of findings are evident for
BRICS region. The adverse effects of financial stress as evident from this research are also
endorsed by the work of Apostolakis and Papadopoulos (2015). Economic policy uncertainty and
financial stress level mostly depend on the fiscal and monetary policy decisions’ uncertainty
resulting from state government unpredictable decision making. If the authorities have ambiguity
in making economic policy decisions and monetary policy decisions, then the indices of global
economic policy uncertainty and financial stress have spikes. At the same time, there is observed
negative change in the financial institution’s stability.

The infectious disease also has harmful consequences for financial markets and financial
institutions (Kilgo et al., 2018). This study is also consistent with the finding of these authors.
The negative impact of infectious diseases on financial systems is evident from different scholarly
works in recent times but it is limited to financial markets (Bai et al., 2020; Bouri et al., 2020). The
findings of this work are endorsing a similar sort of impact on financial institutions’ stability.
Infectious diseases tracker shows changes when there is an outbreak of disease that is epidemic
or pandemic in nature, then the result of this change is observed in the financial system and

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associated markets at the same time. The rising uncertainty due to contagion diseases brings
down the stability level of the financial system. So overall, the inference of this work is consistent
with the developed literature and empirical shreds of evidence.

6. Conclusions
This work provides seminal insights from different strands of uncertainties and the financial
system. The undermined constituent of financial system stability as financial institutions' stability
is rigorously examined in this paper. The financial institutions’ stability is being questioned and
addressed in this work from the contextual setting of the BRICS block. The reviewed literature
provided the base for choosing novel uncertain dimensions as predicting variables of the financial
institutions’ stability. The geopolitical risk, economic policy uncertainty, financial stress, and infec­
tious diseases are found to have a negative and significant impact on the financial institutions’
stability at the country level of BRICS economies. This finding is consistent with the empirical and
theoretical literature.

This seminal work has great importance for policymakers, academicians, and practitioners. The
financial institutions’ stability literature is strengthened, and policymakers can plan to act for
tackling uncertain drivers with a preemptive approach. Financial institutions can use risk manage­
ment models and register to manage these uncertainties as these are now more evident in the
dynamic environment. The ranking of events, which are more influential, can be done from the
findings of this study. The financial institutions’ frameworks for risk management must include an
element of these uncertainties in their planning and implementation for control mechanisms. The
practitioners can become more alert as the findings of this study endorse the impact of uncer­
tainties on financial institutions’ stability and they can prepare proactive strategies to stay ahead
of happenings. The time series analysis is limited to a single economic block, and it must be
extended to all strands of economies. More rigorous research techniques can be levered to endorse
the generalization of the evidence. A series of robustness tests can be applied with alternate
measures of variables for validation of this seminal work. Other uncertain dimensions at the global
level can be benchmarked to examine the impact on financial institutions’ stability in the upcom­
ing studies.

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