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An analysis of network filtering methods to sovereign bond yields during COVID-19

Author

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  • Raymond Ka-Kay Pang
  • Oscar Granados
  • Harsh Chhajer
  • Erika Fille Legara
Abstract
In this work, we investigate the impact of the COVID-19 pandemic on sovereign bond yields. We consider the temporal changes from financial correlations using network filtering methods. These methods consider a subset of links within the correlation matrix, which gives rise to a network structure. We use sovereign bond yield data from 17 European countries between the 2010 and 2020 period. We find the mean correlation to decrease across all filtering methods during the COVID-19 period. We also observe a distinctive trend between filtering methods under multiple network centrality measures. We then relate the significance of economic and health variables towards filtered networks within the COVID-19 period. Under an exponential random graph model, we are able to identify key relations between economic groups across different filtering methods.

Suggested Citation

  • Raymond Ka-Kay Pang & Oscar Granados & Harsh Chhajer & Erika Fille Legara, 2020. "An analysis of network filtering methods to sovereign bond yields during COVID-19," Papers 2009.13390, arXiv.org, revised Feb 2021.
    • Handle: RePEc:arx:papers:2009.13390
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    References listed on IDEAS

    as
    1. Aldasoro, Iñaki & Alves, Iván, 2018. "Multiplex interbank networks and systemic importance: An application to European data," Journal of Financial Stability, Elsevier, vol. 35(C), pages 17-37.
    2. Nicolo Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," Papers 1406.0496, arXiv.org, revised Jan 2015.
    3. Poledna, Sebastian & Molina-Borboa, José Luis & Martínez-Jaramillo, Serafín & van der Leij, Marco & Thurner, Stefan, 2015. "The multi-layer network nature of systemic risk and its implications for the costs of financial crises," Journal of Financial Stability, Elsevier, vol. 20(C), pages 70-81.
    4. Gang-Jin Wang & Chi Xie & Shou Chen, 2017. "Multiscale correlation networks analysis of the US stock market: a wavelet analysis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 561-594, October.
    5. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    6. Dias, João, 2012. "Sovereign debt crisis in the European Union: A minimum spanning tree approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2046-2055.
    7. Musmeci, Nicoló & Aste, Tomaso & Di Matteo, T., 2015. "Relation between financial market structure and the real economy: comparison between clustering methods," LSE Research Online Documents on Economics 61644, London School of Economics and Political Science, LSE Library.
    8. M. Bartolozzi & C. Mellen & T. Di Matteo & T. Aste, 2007. "Multi-scale correlations in different futures markets," Papers 0707.3321, arXiv.org, revised Aug 2007.
    9. Heimo, Tapio & Kaski, Kimmo & Saramäki, Jari, 2009. "Maximal spanning trees, asset graphs and random matrix denoising in the analysis of dynamics of financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(2), pages 145-156.
    10. Antonakakis, Nikolaos & Vergos, Konstantinos, 2013. "Sovereign bond yield spillovers in the Euro zone during the financial and debt crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 258-272.
    11. Tonzer, Lena, 2015. "Cross-border interbank networks, banking risk and contagion," Journal of Financial Stability, Elsevier, vol. 18(C), pages 19-32.
    12. Leonardo Bargigli & Giovanni di Iasio & Luigi Infante & Fabrizio Lillo & Federico Pierobon, 2015. "Interbank markets and multiplex networks: centrality measures and statistical null models," Papers 1501.05751, arXiv.org.
    13. Faruk Balli, 2009. "Spillover effects on government bond yields in euro zone. Does full financial integration exist in European government bond markets?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 33(4), pages 331-363, October.
    14. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    15. A. Verma & R. J. Buonocore & T. Di Matteo, 2019. "A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering," Quantitative Finance, Taylor & Francis Journals, vol. 19(6), pages 981-996, June.
    16. Jenna Birch & Athanasios A. Pantelous & Kimmo Soramäki, 2016. "Analysis of Correlation Based Networks Representing DAX 30 Stock Price Returns," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 501-525, April.
    17. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    18. J.-P. Onnela & K. Kaski & J. Kertész, 2004. "Clustering and information in correlation based financial networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 353-362, March.
    19. Alter, Adrian & Beyer, Andreas, 2014. "The dynamics of spillover effects during the European sovereign debt turmoil," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 134-153.
    20. M. Bartolozzi & C. Mellen & T. Di Matteo & T. Aste, 2007. "Multi-scale correlations in different futures markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 58(2), pages 207-220, July.
    21. Li, Yan & Jiang, Xiong-Fei & Tian, Yue & Li, Sai-Ping & Zheng, Bo, 2019. "Portfolio optimization based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 671-681.
    22. Songtao Wu & Jianmin He & Shouwei Li & Chao Wang, 2018. "Network formation in a multi-asset artificial stock market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(4), pages 1-10, April.
    23. Gilmore, Claire G. & Lucey, Brian M. & Boscia, Marian W., 2010. "Comovements in government bond markets: A minimum spanning tree analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4875-4886.
    24. Musmeci, Nicoló & Nicosia, Vincenzo & Aste, Tomaso & Di Matteo, Tiziana & Latora, Vito, 2017. "The multiplex dependency structure of financial markets," LSE Research Online Documents on Economics 85337, London School of Economics and Political Science, LSE Library.
    25. Beck, Roland & Georgiadis, Georgios & Gräb, Johannes, 2016. "The geography of the great rebalancing in euro area bond markets during the sovereign debt crisis," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 449-460.
    26. Tumminello, Michele & Lillo, Fabrizio & Mantegna, Rosario N., 2010. "Correlation, hierarchies, and networks in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 40-58, July.
    27. Darbellay, Georges A & Wuertz, Diethelm, 2000. "The entropy as a tool for analysing statistical dependences in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 429-439.
    28. Huang, Jingjing & Shang, Pengjian & Zhao, Xiaojun, 2012. "Multifractal diffusion entropy analysis on stock volatility in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5739-5745.
    29. Jang, Wooseok & Lee, Junghoon & Chang, Woojin, 2011. "Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 707-718.
    30. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    31. Montagna, Mattia & Kok, Christoffer, 2013. "Multi-layered interbank model for assessing systemic risk," Kiel Working Papers 1873, Kiel Institute for the World Economy (IfW Kiel).
    32. Dias, João, 2013. "Spanning trees and the Eurozone crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5974-5984.
    33. Claeys, Peter & Vašíček, Bořek, 2014. "Measuring bilateral spillover and testing contagion on sovereign bond markets in Europe," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 151-165.
    34. Hokky Situngkir & Yohanes Surya, 2005. "On Stock Market Dynamics through Ultrametricity of Minimum Spanning Tree," Macroeconomics 0505010, University Library of Munich, Germany.
    35. Nicolò Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2017. "The Multiplex Dependency Structure of Financial Markets," Complexity, Hindawi, vol. 2017, pages 1-13, September.
    36. Di Matteo, T. & Aste, T. & Hyde, S.T. & Ramsden, S., 2005. "Interest rates hierarchical structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 21-33.
    37. Laurent Laloux & Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Random Matrix Theory And Financial Correlations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 391-397.
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    2. Fang, Ming & Taylor, Stephen & Uddin, Ajim, 2022. "The network structure of overnight index swap rates," Finance Research Letters, Elsevier, vol. 46(PB).
    3. Sanjay Kumar Rout & Hrushikesh Mallick, 2022. "Sovereign Bond Market Shock Spillover Over Different Maturities: A Journey from Normal to Covid-19 Period," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(4), pages 697-734, December.
    4. Cécile Bastidon & Myriam Bontonou & Pierre Borgnat & Pablo Jensen & Patrice Abry & Antoine Parent, 2024. "Learning smooth graphs with sparse temporal variations to explore long-term financial trends," Post-Print hal-04731912, HAL.
    5. Ekaterina E. Emm & Gerald D. Gay & Han Ma & Honglin Ren, 2022. "Effects of the Covid‐19 pandemic on derivatives markets: Evidence from global futures and options exchanges," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(5), pages 823-851, May.
    6. Ospina-Forero, Luis & Granados, Oscar M., 2023. "A network analysis of the structure and dynamics of FX derivatives markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).

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