Spatial analysis of sovereign risks: The case of emerging markets

Kışla, Gül Huyugüzel; Önder, A. Özlem

doi:10.1016/j.frl.2017.12.004

Cited by 23 publications

(31 citation statements)

References 32 publications

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“…Moreover, one of the most common problems is endogeneity problem in spatial model estimation. In order to avoid this problem, using of IV/GMM or Quasi Maximum Likelihood (QML hereafter) approach is suggested (Hammadou et al, 2014;Huyugüzel Kışla & Önder, 2018;Kelejian & Piras, 2014;Kondo, 2017;Qu & Lee, 2015). Therefore, the QML approach, which does not require finding reliable instrumental variables, was considered in this study (following Hammadou et al, 2014;Huyugüzel Kışla & Önder, 2018;Kondo, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, it can be interpreted that there is a spatial interaction in the LIS of the EU-13 countries and the UK. If spatial interaction is ignored, biased results can be obtained (Huyugüzel Kışla & Önder, 2018).…”

Section: Empirical Findingsmentioning

confidence: 99%

“…According to LL, the model that has a higher LL value is preferred. We preferred LL criteria, as the criteria which seems to be relatively more robust compare to AIC (e.g Belotti et al, 2017;Huyugüzel Kışla and Önder 2018;Seldadyo et al, 2010)…”

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confidence: 99%

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Economic Integration, Spatial Interaction and Labour Income Share: Evidence from the EU-13 Countries and the UK

Çelik

Karadağ

2021

Appl. Spatial Analysis

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We aim to investigate the effect of Single Market Programme on the spatial interaction of labour income share amongst the EU countries. Spatial panel data analysis is used for the aim of the study. The dataset is obtained from AMECO and WDI databases and covers the time period of 1960-2017. The results show that sign of spatial interaction between these countries in term of the labour income share has turned from positive to negative after the Single Market Programme. In other words, the Single Market Programme has induced to decrease the labour income share by negatively affecting the spatial interaction between these countries. In addition, we find that although the negative impacts of capital-output ratio and trade openness on the labour income share decrease, the negative impact of total factor productivity increases after the Single Market Programme. The results of robustness check also confirm our empirical findings.

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Section: Methodsmentioning

confidence: 99%

Section: Empirical Findingsmentioning

confidence: 99%

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Economic Integration, Spatial Interaction and Labour Income Share: Evidence from the EU-13 Countries and the UK

Çelik

Karadağ

2021

Appl. Spatial Analysis

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“…The researches on international sovereign risk spillovers mainly focus on two aspects. Some studies confirm that international sovereign risk spillovers do exist (Hassan et al, 2017;Kıs ¸la and € Onder, 2018;Ballester et al, 2019). And the results show that there are significant spillover effects (Bostanci and Yilmaz, 2020;Heinz and Sun, 2014).…”

Section: Introductionmentioning

confidence: 91%

“…And the results show that there are significant spillover effects (Bostanci and Yilmaz, 2020;Heinz and Sun, 2014). Some scholars explore spillover channels (Glick and Rose, 1999;Hilscher, 2010;Dell'Erba et al, 2013;Debarsy et al, 2018;Kıs ¸la and € Onder, 2018), and some of them explore sovereign risk transmission channels by using spatial econometrics approach. And they all find that trade channel, financial linkage and geography distance are important spillover channels (Dell'Erba et al, 2013;Buchholz and Tonzer, 2016;Kıs ¸la and € Onder, 2018;Debarsy et al, 2018;Mili, 2018).…”

Section: Introductionmentioning

confidence: 99%

Assessing the role of international transmission channels of sovereign risk: a multidimensional spatial econometrics approach

Huang

2022

IJOEM

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PurposeThis study is the first that aims to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.Design/methodology/approachMultiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.FindingsWith network structure analysis, this study finds that they contain different information content from the perspective of graphical display, node strength and correlation. Developed and emerging countries all play major roles in trade connection, while only developed countries play major roles in financial linkage. Second, by applying the multidimensional SAR model, only the spatial autocorrelation coefficients for trade and financial linkages are significant during the full sample period, which is in sharp contrast to published studies using the SAR model with a single matrix. Third, the spillover channels that play major roles in various periods are different. Only trade channel plays a role during crisis periods and it is the most important. Fourth, the spatial correlation among countries greatly amplifies the shock’s impacts on one market. And spatial effect for developed countries is larger than those for emerging countries, while the mean spatial effect of a unit shock in the USA on emerging countries is slightly greater than that on developed countries.Originality/valueMultiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

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Determinants of financial stress in emerging market economies: Are spatial effects important?

Kösedağlı

Önder

2020

Int. J Fin Econ

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This study aims to analyse the determinants of financial stress and to show the impact of the spatial linkages between 13 emerging economies during 1996–2016. The 2007 Global Financial Crisis has once again showed that financial/economic turmoil of countries has negative effects on other economies. At this point, this study brings novelty to the existing literature by considering the effects of neighbour countries' macroeconomic variables on domestic financial stress and by focusing on financial stress interaction between emerging economies using spatial econometric methods. According to the estimation results, we find current account balance/GDP, economic growth, geopolitical risk and global risk are the most important determinants of financial stress. It is also observed that there is a strong interaction of financial stress among emerging market economies. While geographical linkage is the most important transmission channel of financial stress among those economies, financial and trade linkages also play an important role.

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Spatial analysis of sovereign risks: The case of emerging markets

Cited by 23 publications

References 32 publications

Economic Integration, Spatial Interaction and Labour Income Share: Evidence from the EU-13 Countries and the UK

Economic Integration, Spatial Interaction and Labour Income Share: Evidence from the EU-13 Countries and the UK

Assessing the role of international transmission channels of sovereign risk: a multidimensional spatial econometrics approach

Determinants of financial stress in emerging market economies: Are spatial effects important?

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