De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial

La crisis que se desató en el mercado hipotecario en Estados Unidos en 2008 y que logró propagarse a lo largo de todo sistema financiero, dejó en evidencia el nivel de interconexión que actualmente existe entre las entidades del sector y sus relaciones con el sector productivo, dejando en eviden...

Descripción completa

Detalles Bibliográficos
Autor Principal: Ordóñez Herrera, Juan Sebastián
Otros Autores: Castro, Carlos
Formato: Tesis de maestría (Master Thesis)
Lenguaje:Español (Spanish)
Publicado: Universidad del Rosario 2014
Materias:
Acceso en línea:http://repository.urosario.edu.co/handle/10336/8346
id ir-10336-8346
recordtype dspace
institution EdocUR - Universidad del Rosario
collection DSpace
language Español (Spanish)
topic Econometría Espacial
Externalidad de red
Micro-Prudencial
Macro-Prudencial
Riesgo Sistémico
Economía
Econometría
Banca
Microeconomía
Macroeconomía
Spatial Econometrics
Network Externality
Micro-Prudential
Macro-Prudential
Systemic Risk
spellingShingle Econometría Espacial
Externalidad de red
Micro-Prudencial
Macro-Prudencial
Riesgo Sistémico
Economía
Econometría
Banca
Microeconomía
Macroeconomía
Spatial Econometrics
Network Externality
Micro-Prudential
Macro-Prudential
Systemic Risk
Ordóñez Herrera, Juan Sebastián
De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial
description La crisis que se desató en el mercado hipotecario en Estados Unidos en 2008 y que logró propagarse a lo largo de todo sistema financiero, dejó en evidencia el nivel de interconexión que actualmente existe entre las entidades del sector y sus relaciones con el sector productivo, dejando en evidencia la necesidad de identificar y caracterizar el riesgo sistémico inherente al sistema, para que de esta forma las entidades reguladoras busquen una estabilidad tanto individual, como del sistema en general. El presente documento muestra, a través de un modelo que combina el poder informativo de las redes y su adecuación a un modelo espacial auto regresivo (tipo panel), la importancia de incorporar al enfoque micro-prudencial (propuesto en Basilea II), una variable que capture el efecto de estar conectado con otras entidades, realizando así un análisis macro-prudencial (propuesto en Basilea III).
author2 Castro, Carlos
author_facet Castro, Carlos
Ordóñez Herrera, Juan Sebastián
format Tesis de maestría (Master Thesis)
author Ordóñez Herrera, Juan Sebastián
author_sort Ordóñez Herrera, Juan Sebastián
title De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial
title_short De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial
title_full De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial
title_fullStr De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial
title_full_unstemmed De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial
title_sort de lo micro a lo macro prudencial: un análisis de la supervisión bancaria desde la econometría espacial
publisher Universidad del Rosario
publishDate 2014
url http://repository.urosario.edu.co/handle/10336/8346
_version_ 1645141740736217088
spelling ir-10336-83462019-09-19T12:37:54Z De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial Ordóñez Herrera, Juan Sebastián Castro, Carlos Econometría Espacial Externalidad de red Micro-Prudencial Macro-Prudencial Riesgo Sistémico Economía Econometría Banca Microeconomía Macroeconomía Spatial Econometrics Network Externality Micro-Prudential Macro-Prudential Systemic Risk La crisis que se desató en el mercado hipotecario en Estados Unidos en 2008 y que logró propagarse a lo largo de todo sistema financiero, dejó en evidencia el nivel de interconexión que actualmente existe entre las entidades del sector y sus relaciones con el sector productivo, dejando en evidencia la necesidad de identificar y caracterizar el riesgo sistémico inherente al sistema, para que de esta forma las entidades reguladoras busquen una estabilidad tanto individual, como del sistema en general. El presente documento muestra, a través de un modelo que combina el poder informativo de las redes y su adecuación a un modelo espacial auto regresivo (tipo panel), la importancia de incorporar al enfoque micro-prudencial (propuesto en Basilea II), una variable que capture el efecto de estar conectado con otras entidades, realizando así un análisis macro-prudencial (propuesto en Basilea III). The crisis that erupted in the mortgage market in the United States in 2008 and managed to spread throughout the financial system, demonstrated the level of interconnection that exists between public sector entities and their relationships with the productive sector, leaving highlighted the need to identify and characterize the systemic risk inherent in the system, so that in this way regulators seek both individual as overall system stability. This paper shows, through a model that combines the power of information networks and their suitability for an auto regressive (panel type) spatial model, the importance of incorporating the micro-prudential (proposed Basel II) approach, a variable that captures the effect of being connected with others and making a macro-prudential analysis (proposed Basel III). 2014-05-22 2014-07-11T00:18:58Z info:eu-repo/semantics/masterThesis info:eu-repo/semantics/acceptedVersion http://repository.urosario.edu.co/handle/10336/8346 spa http://creativecommons.org/publicdomain/zero/1.0/ info:eu-repo/semantics/openAccess application/pdf Universidad del Rosario Maestría en Economía Facultad de Economía instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR Acharya, V., & Richardson, M. 2009. Restoring Financial Stability: How to Repair a Failed System. NYU Stern. Aikman, D., Alessandri, P., Eklund, B., Gai, P., Kapadia, S., Martin, E., Mora, N., Sterne, G., & Willison, M. 2009. Funding liquidity risk in a quantitative model of systemic stability. Allen, F., & Babus, A. 2008. Networks in Finance. Wharton School Pub. Chap. 21. Allen, Franklin, & Gale, David. 2000. Financial Contagion. Journal of Political Economy, 108, 1-33. Amini, H., Cont, R., & Minca, A. 2010. Stress testing the resilience of financial networks. Anselin, L. 1980. Estimation methods for spatial autoregressive structures. Regional Science Dissertation & Monograph Series, Program in Urban and Regional Studies, Cornell University. Anselin, L. 1988. Spatial econometrics: methods and models. Vol. 4. Springer. Anselin, L. 2001. Spatial econometrics. A companion to theoretical econometrics. Arias, Mauricio, Mendoza, Juan Carlos, & Perez, D. 2010. Applying CoVaR to Measure Systemic Market Risk: the Colombian Case. Reporte de estabilidad financiera banco de la republica de colombia. Baltagi, B., Song, S. H., & Koh, W. 2003. Testing panel data regression models with spatial error correlation. Journal of econometrics, 117(1), 123-150. Baltagi, B., Egger, P., & Pfa ermayr, M. 2007. A generalized spatial panel data model with random e ects. Tech. rept. Working Paper, Syracuse University. Bastos, E., & Cont, R. 2010. The Brazilian Interbank Network Structure and Systemic Risk. Becher, C., Millard, S., & Soram aki, K. 2008. The network topology of CHAPS Sterling. Bell, K., & Bockstael, N. 2000. Applying the generalized-moments estimation approach to spatial problems involving micro-level data. Review of Economics and Statistics, 82(1), 72-82. Boss, M., Elsinger, H., Summer, M., & Thurner, S. 2003. The Network Topology of the Interbank Market. Boyack, K., Klavans, R., & B orner, K. 2005. Mapping the backbone of science. Scientometrics, 64, No 3, 351-374. Bramoullé, Y., Djebbari, H., & Fortin, B. 2009. Identi cation of peer eff ects through social networks. Journal of Econometrics, 150, 41-55. Brunnermeier, M., Gorton, G., & Krishnamurthy, A. 2011. Risk Topography. Cajueiro, D., & Tabak, B. 2007. The Role of banks in the brazilian interbank market : Does type bank matter? Capera, L., G omez, E., Laverde, M., & Morales, M. 2011. Measuring systemic risk in the Colombian fi nancial system: a systemic contingent claims approach. Temas. Case, A. 1992. Neighbourhood influence and technological change. Regional science and urban economics, 22, 491-508. Castr én, O., & Kavonius, I. 2009. Balance Sheet Interlinkages and Macro-Financial Risk Analysis in the Euro Area. European central bank. Castro, C., & Ferrari, S. 2010 (June). Measuring the systemic importance of fi nancial institutions using market information. Tech. rept. Financial stability review National Bank of Belgium. Chan-Lau, J. 2010a. Balance Sheet Network Analysis of Too-Connected-to-Fail Risk in Global and Domestic Banking Systems. IMF Working Papers. Chan-Lau, J. 2010b. Regulatory Capital Charges for Too-Connected-to-Fail Institutions: A Practical Proposal. IMF Working Papers. Cli ff, A., & Ord, J. 1973. Spatial Autocorrelation. Pion, London. Corrocher, N., & Zirulia, L. 2009. Me and you and everyone we know: An empirical analysis of local network e ffects in mobile communications. Telecommunications Policy, 33(1), 68-79. Crosbie, P., & Bohn, J. 2003. Modeling default risk, Moodys KMV. Das, S., & Uppal, R. 2004. Systemic Risk and International Portfolio Choice. The Journal of Finance, Vol. 59, No. 6, 2809-2834. Degryse, H., & Nguyen, G. 2007. Interbank Exposures: An Empirical Examination of Contagion Risk in the Belgian Banking System. International Journal of Central Banking. Doganoglu, T., & Grzybowski, L. 2007. Estimating network eff ects in mobile telephony in Germany. Information Economics and Policy, 19(1), 65-79. Drehmann, M., & Tarashev, N. 2011. Measuring the Systemic Importance of Interconnected Banks. Drukker, D., Egger, P., & Prucha, I. 2013. On two-step estimation of a spatial autoregressive model with autoregressive disturbances and endogenous regressors. Econometric Reviews, 32(5-6), 686-733. Durlauf, S., & H., Young. 2001. Social Dynamics. MIT: Press. Chap. The new social economics, pages 1-14. ECB. 2010. Recent advances in modelling systemic risk using network analysis. Tech. rept. European Central Bank. Eisenberg, L., & Noe, T. 2001. Systemic Risk in Financial Systems. Management Science, 47, No. 2, 236-249. Elhorst, J. 2003. Speci cation and estimation of spatial panel data models. International regional science review, 26(3), 244-268. Elsinger, H., Lehar, A., & Summer, M. 2006. Using Market Information for Banking System Risk Assessment. Ergungor, O., & Thomson, J. 2006. Systemic banking crises. Vol. 23. Emerald Group Publishing Limited. Espinosa-Vega, M., & Sole, J. 2010. Cross-Border Financial Surveillance: A Network Perspective. Estrada, D., & Morales, P. 2008. La estructura del mercado interbancario y del riesgo de contagio en Colombia. Reporte de estabilidad nanciera banco de la republica de colombia. Flannery, M., & Sorescu, S. 1996. Evidence of bank market discipline in subordinated debenture yields: 1983-1991. The Journal of Finance, 51(4), 1347-1377. Fu, W. 2004. Termination-discriminatory pricing, subscriber bandwagons, and network tra ffic patterns: the Taiwanese mobile phone market. Telecommunications Policy, 28(1), 5-22. Galbiati, M., & Soramaki, K. 2010. An agent-based model of payment systems. Journal of Economic Dynamics & Control. Gauthier, C., Lehar, A., & Souissi, M. 2010. Macroprudential Regulation and Systemic Capital Requirements. Georg, P. 2011 (4). Systemic Risk in Interbank Markets. Ph.D. thesis, Rat der Wirtschaftswissenschaftlichen Fakult at-der-Friedrich-Schiller-Universit at Jena. Gonzalez, C., & Garcia, A. 2002. El sector fi nanciero de cara al siglo XXI. ANIF. Chap. 3, pages 163-185. Gorton, G., & Santomero, A. 1990. Market discipline and bank subordinated debt: Note. Journal of Money, Credit and Banking, 22(1), 119-128. Helbling, Thomas. 2010. What are externalities? Finance & development, 47(4). Hoernig, S. 2008. Tari -mediated network externalities: is regulatory intervention any good? Vol. Huang, X., Zhou, H., & Zhu, H. 2009. A framework for assessing the systemic risk of major fi nancial institutions. Journal of Banking & Finance, 33(11), 2036-2049. IMF. 2009. Global Financial Stability Report. Tech. rept. International Monetary Fund. Jin, F., & Lee, L. 2013. Generalized Spatial Two Stage Least Squares Estimation of Spatial Autoregressive Models with Autoregressive Disturbances in the Presence of Endogenous Regressors and Many Instruments. Econometrics, 1(1), 71-114. Jorion, P., & Zhang, G. 2009. Credit Contagion from Counterparty Risk. THE JOURNAL OF FINANCE, LXIV, 2053. Kambhu, J., Weidman, S., & Krishnan, N. 2007. New Directions for Understanding Systemic Risk. The National Academies Press. Kapoor, M., Kelejian, H., & Prucha, I. 2007. Panel data models with spatially correlated error components. Journal of Econometrics, 140(1), 97-130. Keiler, S., & Eder, A. 2013. CDS Spreads and Systemic Risk-A Spatial Econometric Approach. Kelejian, H., & Prucha, I. 1998. A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. The Journal of Real Estate Finance and Economics, 17(1), 99-121. Kelejian, H., & Prucha, I. 1999. A generalized moments estimator for the autoregressive parameter in a spatial model. International economic review, 40(2), 509-533. Kim, H., & Kwon, N. 2003. The advantage of network size in acquiring new subscribers: a conditional logit analysis of the Korean mobile telephony market. Information Economics and Policy, 15(1), 17-33. Koopman, S., Kr aussl, R., Lucas, A., & Monteiro, A. 2009. Credit cycles and macro fundamentals. Journal of Empirical Finance, 16(1), 42-54. Laeven, L., & Majnoni, G. 2003. Loan loss provisioning and economic slowdowns: too much, too late? Journal of Financial Intermediation, 12(2), 178-197. Lee, L. 2007. Identi cation and estimation of econometric models with group interactions, contextual factors and fixed e ffects. Journal of Econometrics, 140, 333{74. Lee, L.F. 2004. Asymptotic distributions of quasi-maximun likelihood estimators for spatial econometric models. Econometrica, 72, 1899-1926. León, C. 2012. Implied probabilities of default from Colombian money market spreads: The Merton Model under equity market informational constraints. Borradores de Economía. León, C., Machado, C., Cepeda, F., & Sarmiento, M. 2011. Too-connected-to-fail Institutions and Payments Systems Stability: Assessing Challenges for Financial Authorities. Borradores de economia Banco de republica de Colombia. León, Carlos, & Berndsen, Ron J. 2013. Modular scale-free architecture of Colombian fi nancial networks: Evidence and challenges with financial stability in view1. Tech. rept. Banco de la Republica de Colombia. Lin, X. 2005. Peer eff ects and student academic achivement: an application of spatial autoregressive model with group unobservables. Tech. rept. Ohio State University. Liu, M., & Staumy, J. 2010. Systemic Risk Components in a Network Model of Contagion. Lung-fei Lee, X., & Lin, X. 2010. Speci cation and estimation of social interaction models with network structures. Econometrics Journal, 13, 145176. Machado, C., Le on, C., Sarmiento, M., Cepeda, F., Chipatecua, O., & Cely, J. 2010. Riesgo Sist emico y Estabilidad del Sistema de Pagos de Alto Valor en Colombia: An alisis bajo Topolog a de Redes y Simulaci on de Pagos. Borradores de economia Banco de republica de Colombia. Manski, C.F. 1993. Identi cation of endogenous social eff ects: the reflection problem. Review of Economic Studies, 60, 531-42. Markose, S., Giansante, S., Gatkowski, M., & Shaghaghi, A. 2009. Too Interconnected To Fail: Financial Contagion and Systemic risk in network model od CDS and Other Credit Enhancement Obligations of US banks. Mart inez, Constanza, & León, Carlos. 2014. The cost of collateralized borrowing in the Colombian money market: does connectedness matter? Tech. rept. Banco de la Republica de Colombia. Merton, R. 1974. On the pricing of corporate debt: The risk structure of interest rates*. The Journal of Finance, 29(2), 449-470. Márquez, J., & Martinez, S. 2009. A network model of systemic risk: stress testing the banking system. Intelligent Systems In Accounting, Finance And Management, 16, 87-110. Newman, M. 2010. Networks: An Introduction. Oxford University Press. Niera, E., Yanga, J., Yorulmazera, T., & Alentorn, A. 2007. Network models and financial stability. Journal of Economic Dynamics & Control, 31, 2033-2060. on Banking Supervison, Basel Committe. 2010 (December). Basel III: A global regulatory framework for more resilient banks and banking systems. Tech. rept. Bank for international settlements. on Banking Supervison, Basel Committe. 2011. The transmission channels between the financial and real sectors: a critical survey of the literature. Ord, K. 1975. Estimation methods for models of spatial interaction. Journal of the American Statistical Association, 70(349), 120-126. 27 Pesaran, M. 2006. Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967-1012. Pinksey, J., & Slade, M. 2009. The Future of Spatial Econometrics. Tech. rept. The Pennsylvania State University. Signori, D., & Gencay, R. 2012 (March). Economic links and counterparty risk. Tech. rept. Simon Fraser University. Sobolewski, M., & Czajkowski, M. 2012. Network e ects and preference heterogeneity in the case of mobile telecommunications markets. Telecommunications Policy, 36(3), 197-211. Souto, M. 2008. Has the uruguayan nancial system become more resilient to shocks? an analysis adapting the merton framework to a country without equity market data. IMF Occasional Paper. Thomson, J. 2010. On systemically important fi nancial institutions and progressive systemic mitigation. Uhde, A., & Heimesho , U. 2009. Consolidation in banking and nancial stability in Europe: Empirical evidence. Journal of Banking & Finance, 33(7), 1299-1311. Yu, J., de Jong, R., & Lee, L. 2008. Quasi-maximum likelihood estimators for spatial dynamic panel data with fi xed eff ects when both< i> n</i> and< i> T</i> are large. Journal of Econometrics, 146, 118-134.
score 12,131701