Geopolitical risks and financial stress in emerging economies
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NguyenHuu, Tam; Örsal, Deniz Karaman Article — Published Version Geopolitical risks and financial stress in emerging economies The World Economy Provided in Cooperation with: John Wiley & Sons Suggested Citation: NguyenHuu, Tam; Örsal, Deniz Karaman (2023) : Geopolitical risks and financial stress in emerging economies, The World Economy, ISSN 1467-9701, Wiley, Hoboken, NJ, Vol. 47, Iss. 1, pp. 217-237, https://doi.org/10.1111/twec.13529 This Version is available at: https://hdl.handle.net/10419/288209 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/4.0/
World Econ. 2024;47:217–237. | 217 wileyonlinelibrary.com/journal/twec 1 | INTRODUCTION Ushering in an era of great uncertainty, geopolitical risks (GPRs) represent some of the most dangerous threats to the global economy. They are considered seriously threaten the stability of Received: 31 May 2023 | Accepted: 29 September 2023 DOI: 10.1111/twec.13529 ORIGINAL ARTICLE Geopolitical risks and financial stress in emerging economies TamNguyenHuu1 | Deniz KaramanÖrsal2,3 1Center for Methods, Leuphana Universität Lüneburg, Lüneburg, Germany 2Faculty of Business, Economics and Social Sciences, Hamburg Universität, Hamburg, Germany 3Faculty of Business and Economics, Leuphana Universität Lüneburg, Lüneburg, Germany Correspondence Tam NguyenHuu, Center for Methods, Leuphana Universität Lüneburg, Lüneburg, Germany. Email: [email protected] Abstract We investigate the impacts of geopolitical risks (GPRs) on financial stress (FS) in major emerging economies from 1985 to 2019. Applying a recently developed panel quantile estimation method, we show that GPRs pose serious risks to the stability of the financial condition in emerging economies. Namely, when FS is already equal to or above average, GPRs intensify this instability to a remarkable degree. Nevertheless, GPRs do not ignite the stress when the financial situation is benign. In emerging economies, foreign exchange markets and, to a lesser extent, the banking industry and the debt market suffer more severe consequences of geopolitical tensions than the stock market. In contrast, advanced economies, represented by the Group of Seven (G7), have witnessed detrimental consequences of GPRs on their stock markets, but negligible effects on other parts of their financial systems. KEYWORDS banking sector, debt market, emerging economies, financial stress, foreign exchange market, geopolitical risks, stock market This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2023 The Authors. The World Economy published by John Wiley & Sons Ltd.
218 | NGUYENHUU and KARAMAN ÖRSAL the world on many fronts, including economics, politics, and technology (The Economist,2022). These risks can emerge from a wide range of events, both local and global: from the RussoUkrainian War, terrorist attacks, trade disputes, and political gridlock, to climate change, cyberattacks, technology decoupling, and the COVID-19 pandemic (Blackrock Investment Institute,2023). Given the increasingly integrated nature of the world, geopolitical threats can quickly accelerate on a global scale and spread their huge consequences. Understanding the ramifications of geopolitical turmoil on emerging economies is of great interest to many academic, political, and business circles. This article aims to investigate the extent to which the financial stress (FS) in emerging economies can be attributed to geopolitical uncertainties. For a comparison, we also conduct a similar analysis on G7 economies. GPRs have received considerable attention from policymakers and businesses. Mark Carney, the Governor of the Bank of England, argues that GPRs are one of three components of ‘the uncertainty trinity’ in the macroeconomy (Carney,2016). Jerome Powell, the 16th Chair of the Federal Reserve, lists GPRs as one of the major challenges in implementing monetary policies in the US (Powell,2019). The International Monetary Fund (IMF) and the World Bank (WB) have repeatedly identified geopolitical tensions as an important source of instability across the globe (see e.g. IMF,2017–2023, World Bank,2021). And monitoring GPRs has become a fixture in the business agendas of many financial companies, newspapers, and consultancy firms, among others, McKinsey(2016), Morgan(2019), Blackrock Investment Institute(2023), and The Economist(2022). Multiple efforts have been made in the empirical literature to examine the influence of GPRs on certain areas of the financial environments of emerging economies. Among these efforts, three major issues have been identified. First, how can GPRs and FS be properly and systematically measured at a high frequency? Second, how can the typical features of long financial time series in examining the impacts of GPRs be accounted for? Third, how can we provide not only a comprehensive picture of the impacts of GPRs on the whole financial system but also on the subsectors of the financial system? Using monthly data from 1985 to 2019 for 17 major emerging economies, we attempt to address all these issues. We point out that GPRs have pronounced impacts on a global scale. In general, heightened GPRs aggravate stress in financial systems. The impacts are diverse, depending on the subsectors affected, the severity of FS, and the countries being examined. These findings will aid in monitoring global financial markets, managing and preparing for macro risks, and making investment decisions in both normal and turbulent times, all of which have become serious concerns given that geopolitical uncertainties are becoming more complex, interregional, contagious, and increasingly unpredictable. Our paper is divided into five parts. Following this introduction, Section2 briefly reviews the literature on GPRs, FS, and the connection between the two. Sections3 and4 present our data, model, and estimation results. Finally, Section5 concludes. 2 | LITERATURE REVIEW Our research topic pertains to the literature on the consequences of terrorist attacks, wars, conflicts (Eckstein & Tsiddon,2004) and rare disasters (Barro, 2006). The approach of Eckstein and Tsiddon(2004) is an extension of the Blanchard-Yaari model (Blanchard & Fischer,1989). Eckstein and Tsiddon(2004) argue that terror shortens life expectancy and increases the life
| 219 NGUYENHUU and KARAMAN ÖRSAL uncertainty of citizens. Governments react to the consequences of terror by increasing their defense spending, but the amounts they spend cannot offset the damages yielded by terror. As a result, terror reduces investment, output, and consumption. The rare macroeconomic disasters model developed by Barro(2006) resolves the asset-pricing puzzles. Wars are rare disasters that occur infrequently but which can nevertheless cause tremendous harm to the macroeconomy. The huge drops in consumption that accompany such disasters help to explain the dynamics of many financial asset prices and risk premiums over time, such as stocks, real estates, T-bills, exchange rates, and options. The empirical studies conducted in this area provide extensive evidence that geopolitical challenges are a key source of fluctuations in worldwide financial markets. A descriptive study by Ferguson(2008) demonstrates that wars severely affect GDP, consumer prices, exchange rates, inflation, commodity prices, and long-term bond yields in Germany, Russia, the UK, and the US. Baur and Smales(2020) find that stock and bond markets respond adversely to GPRs, but precious metals are resilient when faced with geopolitical challenges. Balcilar etal.(2018) argue that GPRs drive stock market volatility rather than returns for the BRICS economies. Several studies note that geopolitical turbulence can affect financial conditions indirectly through output, investment, trade or consumption [e.g. Cheng and Chiu(2018) for business cycles, Caldara and Iacoviello(2022) for growth and total factor productivity]. However, Egger and Gassebner(2015) find that international terrorism has only a moderate effect on bilateral and multilateral trade and income. Gaibulloev and Sandler(2019) show that terrorism has relatively trivial impacts on the whole economy, while the tourism and investment sectors experience more adverse but ultimately rather transient effects. Regarding methodology, most of the literature on the relationship between GPRs and financial markets employs linear models. To overcome the weaknesses of linear models, though, some recent studies have used non-linear models. For instance, Kösedagli and Önder(2021) apply spatial modelling to examine the drivers of financial instability in emerging economies, finding that GPRs have a significant impact. In addition, Balcilar etal.(2018) use quantile regression to explore the role of GPRs in stock market dynamics in the BRICS countries. However, they do not base their analysis on panel data, which may provide more information than a time series approach (for a single cross-section) in modelling financial dynamics (Hsiao,2007). There are several nontrivial gaps in the empirical literature: the quantification of GPRs and FS, the estimation methods, and the extent of coverage. To fill these gaps, we begin by utilizing the GPR index computed by Caldara and Iacoviello(2022) because of its clear advantages in consistently quantifying GPRs across countries at a monthly frequency. Second, we compute the FS index based on the approach of Balakrishnan etal.(2011), which has been extended by Park and Mercado (2014) and is currently applied by the Asian Development Bank (ADB,2021). This index not only covers a wide range of the subsectors in financial markets, but can also be extended to many emerging economies for a long time horizon at a monthly frequency. Third, for our estimation method, we use quantile regression analysis. While this method offers a high level of flexibility in modeling financial time series, it has scarcely been used in FS research. Finally, by dividing the overall financial sector into different subsectors (stock exchange, bond market, foreign exchange market, and banking sector), we can determine the implications of GPRs for these core parts of the financial system in greater detail; moreover, Saisana and Tarantola(2002) and European Commission and OECD(2008) argue that using subindicators is a pragmatic solution for addressing some of the weaknesses of composite indicators, such as offering overly simplistic and generalised policy advice.
220 | NGUYENHUU and KARAMAN ÖRSAL 3 | MODEL AND DATA We deploy two approaches—fixed-effects models and quantile regression. We start with the fixed-effects estimator to verify major findings in the relevant literature. 3.1 | Econometric model We focus on the quantile regression method because it is well suited to modelling the complex GPRs–FS relationship. Indeed, it provides insights not only into average or mean-to-mean relationships, but also into the relationships at high and low extremes and all other components of the distribution. In our paper, quantile regression answers two questions: Does geopolitical uncertainty affect financial stress symmetrically? How do different GPRs affect FS at different quantiles, especially during extreme episodes? The expansion beyond simple mean analysis is particularly useful because the relationship between GPRs and financial markets can be very different depending on whether the market is experiencing a tranquil or a turbulent period. Furthermore, there are several other properties that make quantile regression useful for studying financial time series: it is robust to outliers, it does not require strict distributional assumptions, and it is robust to a rather heterogeneous error structure (Uribe & Guillen,2020). Several quantile regression estimators for panel data have recently been developed (see Machado and Silva(2019), Galvao and Kato(2017) for a short review). In this paper, we use the approach developed by Machado and Silva(2019): a quantile regression model with individual (‘fixed’) effects. Firpo etal.(2009) call the approach adopted by Machado and Silva(2019) ‘conditional’ quantile regression (CQR) to differentiate it from their own ‘unconditional’ quantile regression (UQR). The quantiles in CQR are not predefined (as in UQR) but, rather, are determined by the control variables. Compared to other ‘conditional’ quantile regression approaches in the literature, Machado and Silva(2019)'s approach has several advantages, including its simple computation, the way in which it allows fixed effects to impact the entire distributions, and its applicability to non-linear models with multiple endogenous variables (Machado & Silva,2019). The general model in Machado and Silva(2019) is: where (𝛼,𝛽,𝜃,𝛾) denote unknown parameters, Z is a k-dimensional vector of the transformations of the components of X as the vector of exogenous independent variables, for element l , Zl=Zl(X) ( l=1, 2, 3, …k ). Fixed-effects for country m are captured by (𝛼m,𝜃m) . We assume that P[𝜃i+Zt,m𝛾>0] =1 , both Xt,m and Ut,m are i.i.d. across m and t , and Ut,m is independent of Xt,m with E(U)=0 and E(|U|)=1 . We want to estimate the conditional quantiles of a random variable Y whose distribution is conditional on a set of explanatory variables X , QY(𝜏|X) as: where the quantile −𝜏 country fixed effect (distribution effect) being 𝛼m(𝜏) ≡ 𝛼m+𝜃mq(𝜏) . This effect might change over different quantiles −𝜏 . With Z=X , Equation1 turns into: Yt,m=𝛼m+𝛽Xt,m+(𝜃m+Zt,m𝛾)Ut,m(m=1, 2, …N;t=1, 2, …T) (1) QY(𝜏|X)=(𝛼m+𝜃mq(𝜏)) +𝛽Xt,m+Zt,m𝛾q(𝜏) (2) QY(𝜏|X)=(𝛼m+𝜃mq(𝜏)) +Xt,m(𝛽+𝛾q(𝜏)).
| 221 NGUYENHUU and KARAMAN ÖRSAL From this equation, we can obtain the coefficients of X 𝛽(𝜏,X)=𝛽+𝛾q(𝜏) and their marginal effects at different quantiles (for details see Machado & Silva,2019). To estimate the quantile coefficients, Machado and Silva(2019) propose the quantiles-via-moments approach. Their simulation study shows that the bias decreased significantly if n∕T<10 , which is clearly our case with monthly data of around 30 years. Different from CQR, in UQR, the changes in the set of control variables do not lead to changes in the quantile ranks. Firpo etal.(2009) estimate the UQR coefficients based on the idea of (recentred) influence function (RIF). It should be noted that CQR and UQR are based on two different concepts, meaning that a direct comparison of the magnitude of coefficients between the two approaches should be done cautiously. For example, CQR shows whether GPRs increase within-group dispersion in cases where the ‘group’ includes the FS indices that have the same values of the explanatory variables (other than GPRs). By contrast, UQR examines whether GPRs will increase the overall dispersion of FS, as indicated by the disparity between different quantiles of the unconditional FS dispersion (see Firpo etal.(2009) and Borah and Basu(2013) for illustrative examples). For the purposes of this paper, we prefer CQR over UQR because CQR has already been widely used in the literature, has a long history dating back to the 1970s with the seminal work of Koenker and Bassett Jr(1978), and is seen by many as the most reliable quantile method. While we acknowledge that UQR uses a sound methodology and has some advantages over CQR, such as its high relevance to policy-making processes (Borah & Basu,2013), our analysis aims to detect the different reactions of financial stress over different quantiles, not merely the marginal effect of GPRs on financial stress, making CQR a perfect fit for our research purpose. Moreover, to avoid too strong a focus on methodological discussion, which is beyond the scope of this study, we employ CQR as our major method (but still use UQR to provide further insights). 3.2 | Data construction and description Our sample comprises 17 major emerging economies: Argentina, Brazil, China, Colombia, Hong Kong, India, Indonesia, Israel, Korea, Malaysia, Mexico, Philippines, Russia, Saudi Arabia, South Africa, Thailand, and Turkey. The selection of economies depends only on the availability of GPRs and FS data. 3.2.1 | GPRs Caldara and Iacoviello(2022) provide data on emerging economies from 1985. Country-specific GPR indices are computed based on the number of articles related to GPRs divided by the total number of the published articles for each month since 1899 in three major newspapers: the New York Times, the Chicago Tribune, and the Washington Post. These articles should reflect geopolitical topics through their use of words related to war, terrorism, or military actors. In quantifying GPRs, the media-based GPR index of Caldara and Iacoviello(2022) has some advantages over the index used by the International Crisis Behaviour database (ICB), which lists manually military-security crises (NguyenHuu,2022). First, in comparison to the ICB, the GPR index can provide a more accurate picture of how investors perceive geopolitical instability because major newspapers are quickly updated and are closely related to investors' interests. Moreover, the frequency of words related to geopolitics might reflect the severity of the risks
222 | NGUYENHUU and KARAMAN ÖRSAL more accurately than the number of actual conflicts recorded by historians, who fail to quantify the monetary or financial damage of conflicts. The newspaper-based index also covers a wider range of geopolitical threats than the ICB index because newspapers can cover complex issues, such as trade disputes and climate change, which are highly relevant to geopolitics but are not explicitly about actual military conflicts. Furthermore, at a high frequency, such as weekly or daily, conflicts can be recorded more accurately by GPR than by ICB because the start and the end dates of actual conflicts are not always clear. Following Caldara and Iacoviello(2022), our variables are standardised for the convenience of interpretation and comparison. Figure1 tracks the diverse dynamics of geopolitical vulnerability in emerging economies in connection to the collapse of the Soviet Union, the turn of the century, the global financial crisis, and the recent years the COVID-19 pandemic. These differences characterise the varied nature of emerging economies' responses to geopolitical situations. 3.2.2 | Aggregate and subsector FS indices An index of financial conditions conveys important signals of the economy's health and is important to economic intervention policies and market dynamics (e.g. Afonso and Jalles(2020) for the responses of sovereign indebtedness to different financial conditions, IMF (2017– 2023) for how global financial stability is evaluated through financial conditions barometer). In the literature, certain indices are used to quantify FS for individual economies. Duprey etal.(2017), for instance, construct the FS indices for EU countries, which are used by the European Central Bank to monitor the financial situation in Europe (European Central FIGURE 1 Geopolitical Risks Index: 1985–2019. [Colour figure can be viewed at wileyonlinelibrary.com]
| 223 NGUYENHUU and KARAMAN ÖRSAL Bank,2021). The benchmark index, meanwhile, incorporates the stock price index, 10-year government bond yields, real effective exchange rates, banking sector stress, and the housing market. Extending this approach to emerging economies can be difficult, though, because of the limited availability of relevant data in the developing world. Another approach is that proposed by Koop and Korobilis(2014), which has been used by IMF to monitor financial situations in major regions of the world (IMF,2017). Finally, to construct the FS index, Koop and Korobilis(2014) use a factor model for a wide range of financial variables. Their approach yields a comprehensive index rather than one that looks separately at subsegments of financial systems, which means that extending their approach to emerging economies over several decades might be challenging. Based on the ideas from Balakrishnan et al. (2011), Park and Mercado (2014), and ADB(2021), our quantification of FS constructs a comparable and complete dataset for all major emerging economies, thus addressing the measurement challenges mentioned above. This approach is excellent for constructing the financial situations of different countries. The subcomponents of this index include banking sector 𝛽 (FS-Bank), currencies market (FSEMPI), debt market (FS-Bond), stock market return (FS-Stk-rt), and stock market volatility (FS-Stk-vol). Moreover, the index not only covers a wide range of subsectors in financial markets, but can also be extended to many emerging economies for a long time horizon at a monthly frequency. The construction of subsector FS indices is based on the following five components: 1. Banking sector 𝛽 measures how risky the banking sector is in comparison to the market as a whole. It measures the relationship between the banking sector stock price index return ( r ) and the overall stock market price index return ( m ). A high 𝛽 value may raise concerns regarding the banking industry risk.1 2. EMPI measures the depreciation of the local currency with respect to the US dollar and the reduction in foreign exchange reserves. High EMPI index signals potential stress in the currencies market. With Δe and ΔRES being month-on-month percent changes in the foreign exchange rate and foreign exchange reserves, respectively, and 𝜎 and 𝜇 being the standard deviation and mean, respectively, we compute the following: 3. To proxy the financial stress in the bond market (FS-Bond), we employ yield differentials between long-term (10-year) local government bonds and US treasury bonds. In the literature, 1Some studies define a threshold of 𝛽 to determine stress level, by converting 𝛽 from continuous values to binary values (0/1) or 0/positive (e.g. Balakrishnan etal.,2011; Cardarelli etal.,2011). Following ADB(2021), we do not convert 𝛽 into any new scale due to three reasons: (1) the determination of threshold or conversion method is arbitrary, (2) a common threshold is not appropriate for a large sample of very heterogenous emerging economies, (3) a conversion may distort the aggregate FS index. We acknowledge that choosing banking sector 𝛽 as the single proxy for banking sector stress can lead to the over-simplification of banking stress index, other candidates can be the banking sector stock volatility, the slope of the yield curve or TED spread (Cardarelli etal.,2011). However it is hard to obtain data for such variables. For other subsector FS indices below, we use the similar approach as we do for banking FS index. (3) 𝛽 = cov(r,m) var(m) (4) EMPI i,t= (Δe i,t −𝜇 i,Δe ) 𝜎 i,Δe − (ΔRES i,t −𝜇 i,ΔRES ) 𝜎 i,ΔRES
224 | NGUYENHUU and KARAMAN ÖRSAL there are many ways to measure sovereign risks (e.g. Popescu & Turcu,2017). We use the sovereign yield spreads in order to gain access to more available data. A large yield spread may reflect instability in the debt market. 4. Stock return (Stk-rt) is calculated as the difference between the current and previous 12month stock price index in natural logarithms. Namely, The stress of the stock market return is computed by multiplying the Stk-rt by minus one, so that higher FS in the stock market suggests a decrease in stock return. Drops in stock return indicate significant problems with the financial condition. 5. Stock volatility (Stk-vol) 𝜎2 is measured using a GARCH (1,1) process as follows: where 𝜎2 and 𝜀 are the variance and error term in the return regression as an autoregressive process with 12 lags. Big swings in the stock market threaten financial stability. Following ADB(2021) and Park and Mercado(2014), we construct the aggregate FS index using principal component analysis and sum up the first two components to represent the overall dynamics of financial conditions. Other methods for constructing a composite FS index have certain disadvantages: simple averaging is biased toward outliers and variance-equal weights might be arbitrary in its selection of weighting methods and often produces erratic and volatile patterns (Park & Mercado,2014). Figure2 illustrates our aggregate FS indices over time. The most turbulent times for most emerging economies occurred around the time of the global financial crisis. Other stressful times coincided with crises at the regional or country level, such as the Asian financial crisis, the Russian default, the Brazil crisis of 1997–1998, the Turkish stock market crash, the outbreak of SARS, and various economic crises in Argentina in the early 2000s. In terms of subsector dynamics, stress levels across subsectors tend to differ and sometimes deviate from the overall market, especially during episodes of high instability. This prompts us to scrutinise the GPR-FS relationship not only at the aggregate but also at subsector levels. For model specification, in addition to GPRs index, we use FS indices of other emerging economies and the G7 countries (to proxy transmission effects), individual country, and global control variables following Balakrishnan etal.(2011), Park and Mercado(2014), and Das etal.(2019). Individual economy control variables include annual GDP growth, fiscal account measured by general government net lending/borrowing as a percentage of GDP, the current account balance as a percentage of GDP, the Chinn-Ito Financial Openness Index, and trade openness as the percentage of trade in GDP. Global control variables include monthly commodity price changes, the global economic activity index, and the LIBOR 3-month rate. All economy-specific control variables are recorded on a yearly basis.2 (5) Stk−rti,t=ln(Stocki,t)−ln(Stocki,t−12) (6) 𝜎2 =𝜔+𝜙 1 𝜀 2 t−1 +𝜙 2 𝜎 2 t−1 2Park and Mercado(2014) interpolates yearly data to create monthly data. In our analysis, the main interest is not to investigate the determinants of FS, moreover, interpolated data is not real data, thus we keep the economy-specific control variables at their original frequency.
| 231 NGUYENHUU and KARAMAN ÖRSAL TABLE 5 GPRs and FS in emerging economies: unconditional quantile regression (UQR). Null hypothesis: Coeff.=0 Test Coeff. Equal. Q1 Q3 Q5 Q7 Q9 Q1=Q5 Q1=Q9 Dep.Var. FS-Agg. FS-Agg. FS-Agg. FS-Agg. FS-Agg. GPRs 0.047 [0.025]* 0.031 [0.017]* 0.033 [0.015]** 0.032 [0.018]* 0.060 [0.031]* 0.4 0.529 0.09 0.760 Dep.Var. FS-Bank FS-Bank FS-Bank FS-Bank FS-Bank GPRs 0.047 [0.022]** 0.037 [0.014]*** 0.001 [0.014] 0.034 [0.018]* 0.004 [0.028] 4.04 0.044 1.83 0.177 Dep.Var. FS-EMPI FS-EMPI FS-EMPI FS-EMPI FS-EMPI GPRs 0.013 [0.020] 0.005 [0.010] 0.022 [0.008]*** 0.044 [0.011]*** 0.087 [0.023]*** 0.25 0.616 7.11 0.008 Dep.Var. FS-Bond FS-Bond FS-Bond FS-Bond FS-Bond GPRs 0.042 [0.019]** 0.070 [0.014]*** 0.101 [0.016]*** 0.156 [0.028]*** 0.074 [0.028]*** 7.21 0.007 1.19 0.275 Dep.Var. FS-Stk-rt FS-Stk-rt FS-Stk-rt FS-Stk-rt FS-Stk-rt GPRs − 0.006 [0.026] 0.006 [0.015] 0.044 [0.011]*** 0.034 [0.011]*** 0.012 [0.023] 3.63 0.057 0.34 0.560 Dep.Var. FS-Stk-vol FS-Stk-vol FS-Stk-vol FS-Stk-vol FS-Stk-vol GPRs 0.002 [0.009] 0.003 [0.007] − 0.003 [0.008] − 0.015 [0.016] − 0.064 [0.030]** 0.15 0.698 6.01 0.014 *, ** and ***: significance at 10%, 5% and 1%, respectively, analytical standard errors in square brackets. Bootstrapping equality test: test statistics on the left, p -value on the right.
232 | NGUYENHUU and KARAMAN ÖRSAL cannot be rejected when FS is at low quantiles, but can be rejected comfortably when the aggregate FS is at high quantiles (at 5% significance level).7 As shown in Table5, the major findings from UQR qualitatively verify our previous findings using CQR. Namely, there is strong evidence that GPRs affect FS at middle and high quantiles. The effects are strong in foreign exchange markets, especially at high quantiles. Similarly, the banking sector and the bond market reflect significant influences of GPRs at low and high quantiles, respectively. However, the UQR results show that GPRs have significant impacts on stock returns and volatility, albeit at only certain quantiles. 4.3 | GPRs and FS in G7 economies Table6 presents the results for advanced economies (G7). Because geopolitical events occur mostly in emerging economies, to measure the impacts of GPRs in advanced economies, we use the global GPRs in all models. Just as they do for emerging economies, GPRs have negative implications for the financial situation in advanced economies. One unit increase in the standard deviation of GPRs leads to a standard deviation increase of around 0.11 in the FS index. One crucial difference between advanced economies and emerging economies is that the impacts of GPRs in advanced economies are similar across quantiles. This indicates that geopolitical disorders from emerging economies might affect financial situations in advanced economies in a rather homogeneous pattern. There are several potential explanations for the differences between emerging and advanced economies in terms of the magnitude of the impacts they have across quantiles. First, the GPR index used in our analysis focuses more on worldwide or more western-oriented risks than on regional or country-specific risks because it only incorporates English-speaking media outlets. Thus, although the examined newspapers are popular and have a wide coverage, they might fail to take into account the important country-specific context of geopolitical events, capture only general information due to limited space for international news, report news relatively late, or ignore the long-time development of events. This is very different to domestic newspapers, which provide more details on the events as well as the relevant context. Several studies have been conducted that use national media sources rather than international ones to investigate the impacts of GPRs on financial market, including those by Jung etal.(2021) and Dibooglu and Cevik(2016). Second, the differing reactions of emerging and advanced economies might be caused by the differences in their economic and social nature. Indeed, readers of newspapers in emerging economies and advanced economies have different backgrounds and country-specific knowledge. Namely, the followers of English newspapers in advanced economies might have a poorer understanding of geopolitical situations in a typical emerging economy than the local people do. The previous literature on the impacts of GPRs in event studies or 7The conclusion drawn from our analysis is based on the null hypothesis of GPRs' coefficient equal to zero. The increase of GPRs' coefficients from 0.037 to 0.047 (Q1 and Q7, respectively, Table4) indicates only a higher withingroup dispersion but presents no clue on between-group dispersion (the overall FS dispersion between different quantiles of the unconditional FS dispersion). We also conducted another test of equality between coefficients of different quantiles. Following Clogg etal.(1995), we calculated Z-statistics ( Z = 𝛽 1 −𝛽 2 √ (SE𝛽 1 )2+(SE𝛽 2 )2, where 𝛽 and SE are coefficient and standard errors, respectively) for the GPRs' coefficients of Q9 and Q1 and find the significant differences in the FS-EMPI and FS-Bond, but not in the FS-Agg and FS-Bank regressions. We prefer bootstrapping test over z -score because using z -scores may require some independence assumption of samples.
| 233 NGUYENHUU and KARAMAN ÖRSAL TABLE 6 GPRs and FS in G7 economies: conditional quantile regression (CQR). Null hypothesis: Coeff.=0 Test Coeff. Equal. Q1 Q3 Q5 Q7 Q9 Q1=Q5 Q1=Q9 Dep.Var. FS-Agg. FS-Agg. FS-Agg. FS-Agg. FS-Agg. GPRs-Global 0.107 [0.042]** 0.112 [0.027]*** 0.115 [0.024]*** 0.120 [0.031]*** 0.126 [0.051]*** 0.25 0.615 0.14 0.711 Dep.Var. FS-Bank FS-Bank FS-Bank FS-Bank FS-Bank GPRs-Global 0.059 [0.031]* 0.045 [0.021]** 0.035 [0.018]* 0.023 [0.023] 0.002 [0.041] 2 0.157 2.24 0.135 Dep.Var. FS-EMPI FS-EMPI FS-EMPI FS-EMPI FS-EMPI GPRs-Global 0.021 [0.083] 0.009 [0.053] 0.002 [0.053] − 0.005 [0.068] − 0.016 [0.111] 1.37 0.243 1.99 0.158 Dep.Var. FS-Bond FS-Bond FS-Bond FS-Bond FS-Bond GPRs-Global 0.013 [0.051] 0.009 [0.035] 0.006 [0.032] 0.002 [0.043] − 0.002 [0.068] 0.76 0.385 0.72 0.398 Dep.Var. FS-Stk-rt FS-Stk-rt FS-Stk-rt FS-Stk-rt FS-Stk-rt GPRs-Global 0.013 [0.032] 0.045 [0.021]** 0.067 [0.017]*** 0.089 [0.020]*** 0.122 [0.033]*** 13.72 0.000 15.11 0.000 Dep.Var. FS-Stk-vol FS-Stk-vol FS-Stk-vol FS-Stk-vol FS-Stk-vol GPRs-Global 0.012 [0.052] 0.032 [0.038] 0.049 [0.030]* 0.069 [0.029]** 0.105 [0.050]** 2.43 0.119 2.88 0.090 *, ** and ***: significance at 10%, 5% and 1%, respectively, analytical standard errors in square brackets. Bootstrapping equality test: test statistics on the left, p -value on the right.
234 | NGUYENHUU and KARAMAN ÖRSAL single-country analyses (see e.g. Balcilar etal.,2018) shows that the impacts are strong only in some specific countries and after some specific events. The impacts in our sample of emerging countries may be less pronounced or visible than in our sample of advanced countries because our sample includes many emerging countries with very diverse development levels, economic structures, or varied resilience levels to shocks and crises. In contrast, because advanced economies are highly connected with mature financial markets and have similar economic and social structures, the impacts of GPRs on advanced countries might be more homogeneous than in emerging economies. In regard to subsectors, there is an even greater difference between advanced and emerging economies. The stock markets in advanced economies see adverse impacts of GPRs on both market return and market volatility. Furthermore, significant and destructive effects are found in high-stress episodes for both measurements of stress. One possible explanation for these impacts may be that the stock markets are internationally connected and emerging markets play a significant role in advanced economies. Therefore, the spillover effect of shocks from the outside world, especially from emerging economies, is sizeable. This result is consistent with Chesney etal.(2011), who find that 77 large-scale terrorist events (around 80% of which occurred in emerging economies) have had significant impacts on advanced economies. In contrast, other sections of the financial system, such as foreign exchange markets, bond markets, and banking sectors, are almost unaffected by geopolitical uncertainties in emerging economies. This apparent lack of correlation can be explained by the way the stress indices of these subsectors are aggregated. The EMPI is constructed by using foreign exchange rates and reserves, and the FS index in debt markets measures government bond spreads. All these components are largely driven by domestic factors, with government policies playing an essential role. In other words, the subsectors of all advanced economies are more reliant on the subsectors of the US economy rather than on other emerging economies' subsectors. The banking sectors of advanced economies suffer only some disruptive impacts of GPRs at low quantiles. 5 | CONCLUSIONS Our paper finds that GPRs play a prominent role in shaping the financial conditions of emerging economies. Our main results show that, in emerging economies, foreign exchange markets and, to a lesser degree, the banking and debt sectors might be among the hardest-hit areas. Our quantile analyses establish that the magnitude of the impacts is largely driven by the stress level of the corresponding markets. These profound effects are not observed in stock markets, though, which tend to be relatively robust to external disturbances from geopolitical events. This is in stark contrast to advanced economies, where GPRs have major impacts that are concentrated mostly on the stock markets. These findings could prove useful for both political and business decision-makers. For instance, based on our results, we recommend that appropriate reaction plans be made to prepare for blooming geopolitical uncertainties, especially when the financial markets reveal certain stress signals. Moreover, reaction plans should take into account different policies for different subsectors because GPRs do not affect all subsectors equally. Investors, meanwhile, should consider the fragility of the relevant asset markets when they build up or adjust their portfolios.
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