Sovereign Debt Risk under Geopolitical Shocks: Spillovers and Transmission Channels
LUAN Xi, ZHU Huanjun, PENG Yujie, XU Qiyuan
Institute of World Economics and Politics, Chinese Academy of Social Sciences; Wang Yanan Institute for Studies in Economics, School of Economics, Paula and Gregory Chow Institute for Studies in Economics, Xiamen University; School of Management and Economics, The Chinese University of Hong Kong, Shenzhen; Institute of World Economics and Politics, Chinese Academy of Social Sciences
Summary:
Today's world is amid great changes that have not been seen in a century. The intensification of geopolitical conflicts has significantly impacted sovereign debt credit. Early literature mainly studied sovereign debt credit risk based on economic logic, with less attention on international political shocks. However, the IMF's 2023 Global Financial Stability Report emphasized that an increase in geopolitical risks would raise sovereign debt risks. Geopolitical upheavals (e.g., the Ukraine crisis in 2022) not only have an impact on the countries directly involved, but may also trigger broader economic and political changes through transnational spillovers.
By reviewing existing literature, studies on the direct impact of geopolitical shocks on sovereign debt risk are relatively abundant, but the conclusions are not consistent and still require in-depth exploration. Research on the transnational spillover effects of geopolitical shocks is relatively scarce. Furthermore, there are limitations in the literature in terms of research data, methodology, and sample size. In this context, this paper refers to Caldara and Iacoviello (2022) and improves and expands the proxy variable for geopolitical shocks based on the Dow Jones News Database. This substantial improvement avoids bias at the country level. In terms of research methodology, this paper uses the dynamic spatial Durbin model to empirically study the transnational spillover of geopolitical shocks on sovereign debt risk and its channels. When setting up the spatial distance matrix, this paper, in line with the characteristics of geopolitics, not only considers geographical distance but also pays special attention to the impact of bilateral political relations, in order to fully capture the spatial spillover information at the country level. By finding the optimal nested matrix method, the paper analyzes the spillover channels to explore which type of relationship between countries plays the most critical role in the spillover. On this basis, the paper also examines the heterogeneity of spatial spillover under different subsample groups and the spatial spillover effects of different types of geopolitical shocks on sovereign debt risk. Finally, the paper conducts robustness tests on the main conclusions to ensure the reliability of the research results.
Combining monthly data from 65 sovereign countries from 2017 to 2023, this paper finds that geopolitical shocks have a significant spatial spillover effect on the sovereign debt risk of geographical neighbors and political “neighbors,” including direct and indirect effects. According to the optimal nested matrix matching, the “entanglement” effect based on bilateral political relations is the main channel for the spatial spillover effects. By grouping samples according to whether they are NATO member countries and their trade association with Russia, it is found that the impact and spillover of geopolitical shocks on the sovereign debt risk of NATO member countries and countries with high trade association with Russia are more significant. Looking at the classification of geopolitical shocks, geopolitical actions rather than geopolitical threats are the main factors affecting sovereign debt risk and spatial spillover.
The marginal contributions of this paper are as follows:
First, it solves the bias problem of the original country-specific geopolitical risk index. Building on the research of Caldara and Iacoviello (2022), this study constructs a monthly country-specific geopolitical risk index using the Dow Jones News Database by expanding the media sample range and precisely defining news categories. This index effectively overcomes the bias, event omission, and misestimating problems caused by the original index based on a small amount of data from American newspapers and gets rid of the geopolitical narrative bias from the perspective of American media. In addition, this paper expands the sample countries to 65 sovereign states, increasing the sample size in relevant empirical research.
Second, the paper finds through the dynamic spatial Durbin model that geopolitical shocks have a more widespread country-specific mutual spillover on the sovereign debt risk of other countries. This is different from the previous literature that studied the one-way spillover of geopolitical risks in a certain country or region to other economies.
Third, this paper broadens the research perspective, depicts the important role of bilateral political relations in the spatial spillover of geopolitical shocks on sovereign debt risk, and tests the spatial spillover effects under special political relations (military alliance). By finding the optimal nested weight combination, it is discovered that the “entanglement” effect based on political relations is the main channel for risk spillover. Previously, the empirical analysis of bilateral political relations in economics mainly focused on trade investment and international aid, with less application in the areas of sovereign debt and spatial spillover effects.
Fourth, this paper constructs a country-level geopolitical risk category index according to whether actual actions are taken, referring to the global geopolitical risk category classification of Caldara and Iacoviello (2022). We further investigate the impact and spillover differences of different types of geopolitical shocks.
[1]卜林、孙丽玲和李政,2020,《地缘政治风险、经济政策不确定性与股票市场波动》,《南开经济研究》第5期,第185~205页。 [2]陈继勇、雷欣和黄开琢,2010,《知识溢出、自主创新能力与外商直接投资》,《管理世界》第7期,第30~42页。 [3]李政、刘淇和鲁晏辰,2020,《主权债务风险跨国溢出研究——来自频域的新证据》,《金融研究》第9期,第59~77页。 [4]李政、石晴和卜林,2021,《地缘政治风险是国际原油价格波动的影响因子吗？基于GARCH-MIDAS模型的分析》,《世界经济研究》第11期,第18~32+135页。 [5]刘辰辉和唐世平,2023,《机器学习在冲突预测方面的局限》,《世界经济与政治》第12期,第114~143页。 [6]逄锐之,2022,《权力转移、地缘距离与大国战和关系研究》,《世界经济与政治》第9期,第60～98页。 [7]覃成林和杨霞,2017,《先富地区带动了其他地区共同富裕吗——基于空间外溢效应的分析》,《中国工业经济》第10期,第44~61页。 [8]王建新,2022,《国际多边开发银行与全球贸易增长:以加入亚投行为例》,《世界经济》第12期,第3~28页。 [9]徐奇渊、孙靓莹和熊婉婷,2023,《发展中国家主权债务问题:一个系统、全面、有效的综合解决框架》,《拉丁美洲研究》第2期,第2~17页。 [10]Afonso, A., J. Alves and S. Monteiro, 2023, “Beyond Borders: Assessing the Influence of Geopolitical Tensions on Sovereign Risk Dynamics”, REM Working Paper, 0300~2023. [11]Bailey, M. A., A. Strezhnev and E. Voeten, 2017, “Estimating Dynamic State Preferences from United Nations Voting Data”, The Journal of Conflict Resolution, 61(2), pp. 430~456. [12]Balli, F., H. O. Balli, M. Hasan and R. Gregory-Allen, 2022, “Geopolitical Risk Spillovers and its Determinants”, The Annals of Regional Science,68(2), pp. 463~500. [13]Bianchi, J. and C. Sosa-Padilla, 2022, “On Wars, Sanctions and Sovereign Default”, Journal of Monetary Economics, 141, pp. 62~70. [14]Bianchi, J. and J. Mondragon, 2022, “Monetary Independence and Rollover Crises”, The Quarterly Journal of Economics, 137(1), pp. 435~491. [15]Caldara, D. and M. Iacoviello, 2022, “Measuring Geopolitical Risk”, American Economic Review, 112(4), pp. 1194~1225. [16]Caldara, D., S. Conlisk, M. Iacoviello and M. Penn, 2024, “Do Geopolitical Risks Raise or Lower Inflation？”, Federal Reserve Board of Governors. [17]Daehler, T. B., J. Aizenman and Y. Jinjarak, 2021, “Emerging Markets Sovereign CDS Spreads during COVID-19: Economics versus Epidemiology News”, Economic Modelling, pp.100,105504. [18]Debarsy, N., C. Dossougoin, C. Ertur and J. Y. Gnabo, 2018, “Measuring Sovereign Risk Spillovers and Assessing the Role of Transmission Channels: A Spatial Econometrics Approach”, Journal of Economic Dynamics ＆ Control, 87, pp. 21~45. [19]Debarsy, N. and J. P. Lesage, 2021, “Using Convex Combinations of Spatial Weights in Spatial Autoregressive Models”, Handbook of Regional Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2267~2282. [20]Elhorst, J. P., D. J. Lacombe and G. Piras, 2011, “On Model Specification and Parameter Space Definitions in Higher Order Spatial Econometric Models”, Regional Science and Urban Economics, 42,pp.211~220. [21]Elhorst, J. P., 2014, Spatial Econometrics: from Cross-Sectional Data to Spatial Panels. Published by Berlin: Springer Briefs in Regional Science. [22]Gopinath, G., P. O. Gourinchas, A. F. Presbitero and P. Topalova, 2024, “Changing Global Linkages: A New Cold War？”, IMF Working Papers, No. 2024/076. [23]Guriev, S. and N. Melnikov, 2016, “War, Inflation, and Social Capital”, American Economic Review, 106(5), pp. 230~235. [24]Gupta, R., G. Gozgor, H. Kaya and E. Demir, 2019, “Effects of Geopolitical Risks on Trade Flows: Evidence from the Gravity Model”, Eurasian Economic Review, 9(2), pp. 515~530. [25]IMF, 2023, “Geopolitics and financial fragmentation: Implications for Macro-financial Stability”, The Economic Risks from a Fractured World Economy, https://www.imf.org/en/Publications/GFSR/Issues/2023/04/11/global-financial-stability-report-april-2023. [26]Lee, L. F. and J. Yu, 2014. “Efficient GMM Estimation of Spatial Dynamic Panel Data Models with Fixed Effects”, Journal of Econometrics, 180(2), pp.174~197. [27]Mazarei A., 2023, “Debt Clouds over the Middle East”, https://www.imf.org/en/Publications/fandd/issues/2023/09/debt-clouds-over-the-middle-east-adnan-mazarei. [28]Press-Barnathan, G., 2006, “Managing the Hegemon: NATO under Unipolarity”, Security Studies, 15(2), pp.271~309. [29]Procasky, W. J. and N. U. Ujah, 2016, “Terrorism and its Impact on the Cost of Debt”, Journal of International Money and Finance, 60, pp. 253~266. [30]Snyder, G. H., 1984, “The Security Dilemma in Alliance Politics”, World Politics, 36(4),pp.461~495. [31]Zheng, H. H., 2023, “Sovereign Debt Responses to the COVID-19 Pandemic”, Journal of International Economics, 143, 103766. [32]Zheng, J. L., B. Y. Wen, Y. H. Jiang, X. H. Wang and Y. Shen, 2023, “Risk Spillovers across Geopolitical Risk and Global Financial Markets”, Energy Economics, 127(A), 107051.
