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

En este trabajo, investigamos el impacto de la pandemia COVID-19 en los rendimientos de los bonos soberanos. soberanos. Consideramos los cambios temporales de las correlaciones financieras utilizando métodos de filtrado de redes. métodos de filtrado de redes. Estos métodos consideran un subconjunto...

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Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Universidad de Bogotá Jorge Tadeo Lozano

Repositorio:: Expeditio: repositorio UTadeo

Idioma:: spa

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dc.title.spa.fl_str_mv	An analysis of network filtering methods to sovereign bond yields during COVID-19
title	An analysis of network filtering methods to sovereign bond yields during COVID-19
spellingShingle	An analysis of network filtering methods to sovereign bond yields during COVID-19 Ecofísica Correlaciones financieras Crisis Pandemia de COVID-19, 2020-
title_short	An analysis of network filtering methods to sovereign bond yields during COVID-19
title_full	An analysis of network filtering methods to sovereign bond yields during COVID-19
title_fullStr	An analysis of network filtering methods to sovereign bond yields during COVID-19
title_full_unstemmed	An analysis of network filtering methods to sovereign bond yields during COVID-19
title_sort	An analysis of network filtering methods to sovereign bond yields during COVID-19
dc.subject.spa.fl_str_mv	Ecofísica Correlaciones financieras Crisis
topic	Ecofísica Correlaciones financieras Crisis Pandemia de COVID-19, 2020-
dc.subject.lemb.spa.fl_str_mv	Pandemia de COVID-19, 2020-
description	En este trabajo, investigamos el impacto de la pandemia COVID-19 en los rendimientos de los bonos soberanos. soberanos. Consideramos los cambios temporales de las correlaciones financieras utilizando métodos de filtrado de redes. métodos de filtrado de redes. Estos métodos consideran un subconjunto de enlaces dentro de la matriz de correlación, lo que da lugar a una estructura de red. que da lugar a una estructura de red. Utilizamos datos de rendimiento de bonos soberanos de 17 países europeos entre 2010 y 2020. Encontramos que la correlación correlación disminuye en todos los métodos de filtrado durante el período COVID-19. También También observamos una tendencia distintiva entre los métodos de filtrado bajo múltiples medidas de centralidad de red. A continuación, relacionamos la importancia de las variables económicas y sanitarias con las redes filtradas durante el periodo COVID-19. las redes filtradas durante el periodo COVID-19. Bajo un modelo de grafo aleatorio exponencial con un modelo de grafo aleatorio exponencial, podemos identificar relaciones clave entre los grupos económicos según los distintos métodos de filtrado.
publishDate	2021
dc.date.created.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2023-10-12T16:00:09Z
dc.date.available.none.fl_str_mv	2023-10-12T16:00:09Z
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dc.relation.references.spa.fl_str_mv	Y. Li, X.-F. Jiang, Y. Tian, S.-P. Li, B. Zheng, Portfolio optimization based on network topology, Physica A 515 (2019) 671–681. S.K. Stavroglou, A.A. Pantelous, K. Soramaki, K. Zuev, Causality networks of financial assets, J. Netw. Theory Finance 3 (2016) 17–67. S.E. Maeng, H.W. Choi, J.W. Lee, Complex networks and minimal spanning trees in international trade network, Int. J. Mod. Phys.: Conf. Ser. 16 (2012) 50–60. C. Leon, K. Leiton, J. Perez, Extracting the sovereigns’ CDS market hierarchy: A correlation-filtering approach, Physica A 415 (2014) 407–420. C.G. Gilmore, B.M. Lucey, M.W. Boscia, Comovements in government bond markets: A minimum spanning tree analysis, Physica A 389 (2010) 4875–4886. L. Laloux, P. Cizeau, M. Potters, J.-P. Bouchaud, Random matrix theory and financial correlations, Int. J. Theor. Appl. Finance 3 (03) (2000) 391–397. V. Plerou, P. Gopikrishnan, B. Rosenow, L.A.N. Amaral, T. Guhr, H.E. Stanley, Random matrix approach to cross correlations in financial data, Phys. Rev. E 65 (6) (2002) 066126. L.S. Junior, I.D.P. Franca, Correlation of financial markets in times of crisis, Physica A 391 (1–2) (2012) 187–208. L. Junior, A. Mullokandov, D. Kenett, Dependency relations among international stock market indices, J. Risk Financ. Manage. 8 (2) (2015) 227–265. J. Huang, P. Shang, X. Zhao, Multifractal diffusion entropy analysis on stock volatility in financial markets, Physica A 391 (22) (2012) 5739–5745.
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spelling	2023-10-12T16:00:09Z2023-10-12T16:00:09Z2021https://pdf.sciencedirectassets.com/271529/1-s2.0-S0378437121X00088/1-s2.0-S0378437121002673/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEOf%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJIMEYCIQCDbQ0qfXyx3OgS%2BO%2BnHPE%2Br%2Bapdmg9EM66vOPTLCkMxQIhAL1MYcU7c9oEaUnYWVIncOWWpMZd3DQ%2BE1NhYc%2FlOFBrKrwFCO%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQBRoMMDU5MDAzNTQ2ODY1IgwHewUXCWqSMCccO5MqkAXVR0u2fdm0E3eFNhhw%2BSvx7Z6o46Oi6c6PFB6Zr28uLv1I0Gwm76UDphee8WAACV81hBVca%2F%2FrDlJV2af8mrEVKUY3ZS%2BOe5kaKIewd0l4W5pp50BW7kLZ9mwx%2BjNijc6nUKqgZg0igPtfsneA05UXBaz7feNg8lV0ORrv13TDpQCywnuZgDN1ZSEW%2Fb1sLiNOANcp5EqVP2Wmk3D2G9UaJexCuapHf4eBPkVZW15HGzk%2FpjNU7pERgyZeFSUJY%2F2UVVdZ7lL5wILu8M8vdmPMQ0niHn4pA%2BopQmpEUdURAmINbN10vRnetd0f64VBdRih8TGMa%2FYtikAQ%2FnDDSHorDffMvDqBbmZPF9q87C4gzNGxkw%2Bd4aLn%2BFV7tw3lRQQw7p8tohyNv4BGBEfT11Ba56LOwQdWAwrW4NXNRCY7XE7NRMkNYTfjeU2199TUJjLOMQpyBPqNeEEg5dwGruKUL5feSy3OHeYlsIfHjC1ByqJ3sOJ2uB4SeQQzQFMXtJoYkRzjj%2BiY2QX8D5bRii78kdczhAP1M8j%2B7ehdRaHbs63OInTNkYW2cC%2Ftss3IshzEw33dkNtiusFHU0UVLVCd%2FGYEYjzBzAMrmkFwofzQgPPOm903OR0e72reOjpPSjOZveWborHAS08F%2Ff5ia6LLjiG574WZLTttdrXORn7LdxXIeb%2F1rMcE5E4w3i%2FGla9QhpGA7grZyWHXD1zf1fHvsHMj9mxProOq3LVdLZWhfml%2BcdPnNMQtEafEh85tyc2CCRTIp8Jq%2FZx4aeTU8YAVvn7ealslPmZzyMNrBKVh%2FxyISSZgsSrUQpMAWBbQ1cefD%2Bk88jvdV45GhlAYOh62cSAql4YTRVNprWHEAM4wYTCM3pqpBjqwAfNw0faycwbj1DSNvlCYbDgi3vz0UlVw%2FqX4n3gSRHJP7xxWGun%2FBRdmK3uenbF9B%2BwR2yh09g9qny6u17HBskzMktrl9aPFitmUJQViUuVbvaDDsZYBnV3%2BG3wprcxyzuZhIMG8mYUUsKwus0i8l%2BOAfrb12%2Bf4Xaq76CKUgBFenqGRVvske4FYIu%2FYu8aq4X6kdYshtxyB%2FW9P8039Q7qonJs5ylmgTAm9dVm%2B6yZS&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20231011T151410Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYZ5XEGAH2%2F20231011%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7e4e24b07dc8a6788c88e756123008e632028ca82f91f53e2d32ce9ecb5a1180&hash=834ab04b47471a9f1019f27a8afaa3903e07f126a9105f13ae490755b5fc983d&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0378437121002673&tid=spdf-268f55ec-91be-4e12-ba7d-784cf5cf12f0&sid=ddbc81cc76906440c72a7ab1d25639fa8bd5gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=19095a55515a520b545607&rr=81480d18ba61f7b8&cc=cohttp://hdl.handle.net/20.500.12010/32194En este trabajo, investigamos el impacto de la pandemia COVID-19 en los rendimientos de los bonos soberanos. soberanos. Consideramos los cambios temporales de las correlaciones financieras utilizando métodos de filtrado de redes. métodos de filtrado de redes. Estos métodos consideran un subconjunto de enlaces dentro de la matriz de correlación, lo que da lugar a una estructura de red. que da lugar a una estructura de red. Utilizamos datos de rendimiento de bonos soberanos de 17 países europeos entre 2010 y 2020. Encontramos que la correlación correlación disminuye en todos los métodos de filtrado durante el período COVID-19. También También observamos una tendencia distintiva entre los métodos de filtrado bajo múltiples medidas de centralidad de red. A continuación, relacionamos la importancia de las variables económicas y sanitarias con las redes filtradas durante el periodo COVID-19. las redes filtradas durante el periodo COVID-19. Bajo un modelo de grafo aleatorio exponencial con un modelo de grafo aleatorio exponencial, podemos identificar relaciones clave entre los grupos económicos según los distintos métodos de filtrado.#Covid-19In 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.12 páginasapplication/pdfspaPhysica AEcofísicaCorrelaciones financierasCrisisPandemia de COVID-19, 2020-An analysis of network filtering methods to sovereign bond yields during COVID-19Acceso restringidohttp://purl.org/coar/access_right/c_16ecY. Li, X.-F. Jiang, Y. Tian, S.-P. Li, B. Zheng, Portfolio optimization based on network topology, Physica A 515 (2019) 671–681.S.K. Stavroglou, A.A. Pantelous, K. Soramaki, K. Zuev, Causality networks of financial assets, J. Netw. Theory Finance 3 (2016) 17–67.S.E. Maeng, H.W. Choi, J.W. Lee, Complex networks and minimal spanning trees in international trade network, Int. J. Mod. Phys.: Conf. Ser. 16 (2012) 50–60.C. Leon, K. Leiton, J. Perez, Extracting the sovereigns’ CDS market hierarchy: A correlation-filtering approach, Physica A 415 (2014) 407–420.C.G. Gilmore, B.M. Lucey, M.W. Boscia, Comovements in government bond markets: A minimum spanning tree analysis, Physica A 389 (2010) 4875–4886.L. Laloux, P. Cizeau, M. Potters, J.-P. Bouchaud, Random matrix theory and financial correlations, Int. J. Theor. Appl. Finance 3 (03) (2000) 391–397.V. Plerou, P. Gopikrishnan, B. Rosenow, L.A.N. Amaral, T. Guhr, H.E. Stanley, Random matrix approach to cross correlations in financial data, Phys. Rev. E 65 (6) (2002) 066126.L.S. Junior, I.D.P. Franca, Correlation of financial markets in times of crisis, Physica A 391 (1–2) (2012) 187–208.L. Junior, A. Mullokandov, D. Kenett, Dependency relations among international stock market indices, J. Risk Financ. Manage. 8 (2) (2015) 227–265.J. Huang, P. Shang, X. Zhao, Multifractal diffusion entropy analysis on stock volatility in financial markets, Physica A 391 (22) (2012) 5739–5745.http://purl.org/coar/resource_type/c_6501Ka-Kay Pang, RaymondGranados, Oscar M.Chhajer, HarshTHUMBNAILArtículo de investigación indexado con enlace de consulta de manera referencial (2).pdf.jpgArtículo de investigación indexado con enlace de consulta de manera referencial (2).pdf.jpgIM Thumbnailimage/jpeg8825https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/32194/5/Art%c3%adculo%20de%20investigaci%c3%b3n%20indexado%20con%20enlace%20de%20consulta%20de%20manera%20referencial%20%282%29.pdf.jpg8f22f8006469106381df5badf16cfc1bMD55open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-82938https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/32194/2/license.txtbaba314677a6b940f072575a13bb6906MD52open accessORIGINALArtículo de investigación indexado con enlace de consulta de manera referencial (2).pdfArtículo de investigación indexado con enlace de consulta de manera referencial (2).pdfapplication/pdf48864https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/32194/3/Art%c3%adculo%20de%20investigaci%c3%b3n%20indexado%20con%20enlace%20de%20consulta%20de%20manera%20referencial%20%282%29.pdfad3fe84c3dde038025c42938f551e755MD53embargoed access\|\|\|2223-10-12METADATA1-s2.0-S0378437121002673-main.pdf1-s2.0-S0378437121002673-main.pdfapplication/pdf577679https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/32194/4/1-s2.0-S0378437121002673-main.pdf051f502ec71776082d20425dca658720MD54embargoed access\|\|\|2223-10-1220.500.12010/32194oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/321942023-10-13 03:02:16.984embargoed access\|\|\|2223-10-12Repositorio Institucional - Universidad Jorge Tadeo Lozanoexpeditiorepositorio@utadeo.edu.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

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

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