IDEAS home Printed from https://ideas.repec.org/p/bdr/temest/062.html
   My bibliography  Save this paper

Relación entre el riesgo sistémico del sistema financiero y el sector real

Author

Listed:
  • Wilmar Cabrera
  • Javier Gutiérrez Rueda
  • Juan Carlos Mendoza
  • Luis Fernando Melo

Abstract

En este documento se analiza la relación existente entre el riesgo del sector real y del sistema financiero. Para esto, se estima un modelo FAVAR en el cual se incluyen un conjunto de variables que reflejan la evolución de la dinámica común de las series de los diferentes sectores de la economía y un componente idiosincrático. Dado el proceso generador de datos identificado en el modelo antes mencionado, es posible estimar las medidas de riesgo del sistema financiero y del sector real utilizando la metodología de regresión por cuantiles. Posteriormente, se usa la medida de CoV aR, propuesta por Adrian & Brunnermeier (2011) para medir el grado de codependencia entre los riesgos de estos sectores. Los resultados muestran que los indicadores de riesgo reflejan las situaciones de estrés que se han presentado en el sector real y el financiero de la economía colombiana. Adicionalmente, mediante las estimaciones del modelo FAVAR se realiza un análisis de impulso respuesta para analizar cómo se trasmiten choques adversos entre un sector y otro.

Suggested Citation

  • Wilmar Cabrera & Javier Gutiérrez Rueda & Juan Carlos Mendoza & Luis Fernando Melo, 2011. "Relación entre el riesgo sistémico del sistema financiero y el sector real," Temas de Estabilidad Financiera 062, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:temest:062
    DOI: 10.32468/tef.62
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/tef.62
    Download Restriction: no
    File URL: https://libkey.io/10.32468/tef.62?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Glenn Hoggarth & Steffen Sorensen & Lea Zicchino, 2005. "Stress tests of UK banks using a VAR approach," Bank of England working papers 282, Bank of England.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    4. Miguel Morales & Dairo Estrada, 2010. "A financial stability index for Colombia," Annals of Finance, Springer, vol. 6(4), pages 555-581, October.
    5. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wilmar Alexander Cabrera Rodríguez & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Relación entre el riesgo sistémico del sistema financiero y el sector real: un enfoque FAVAR," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 32(75), pages 1-22, December.
    2. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2014. "Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 693-714, October.
    3. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
    4. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    5. Darracq Pariès, Matthieu & Maurin, Laurent & Moccero, Diego, 2014. "Financial conditions index and credit supply shocks for the euro area," Working Paper Series 1644, European Central Bank.
    6. Luke Hartigan & James Morley, 2020. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 271-293, September.
    7. Joseph P Byrne & Ryuta Sakemoto & Bing Xu, 2020. "Commodity price co-movement: heterogeneity and the time-varying impact of fundamentals [Oil price shocks and the stock market: evidence from Japan]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(2), pages 499-528.
    8. Pellényi, Gábor, 2012. "A monetáris politika hatása a magyar gazdaságra. Elemzés strukturális, dinamikus faktormodellel [The sectoral effects of monetary policy in Hungary: a structural factor]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 263-284.
    9. Amendola, Adalgiso & Di Serio, Mario & Fragetta, Matteo & Melina, Giovanni, 2020. "The euro-area government spending multiplier at the effective lower bound," European Economic Review, Elsevier, vol. 127(C).
    10. Chen, Bin-xia & Sun, Yan-lin, 2022. "The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    11. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    12. Jean Boivin & Marc P. Giannoni & Dalibor Stevanović, 2020. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 272-284, April.
    13. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    14. Jean-Stéphane Mésonnier & Dalibor Stevanovic, 2012. "Bank Leverage Shocks and the Macroeconomy: a New Look in a Data-Rich Environment," CIRANO Working Papers 2012s-23, CIRANO.
    15. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.
    16. Lukas Berend & Jan Pruser, 2024. "The Transmission of Monetary Policy via Common Cycles in the Euro Area," Papers 2410.05741, arXiv.org, revised Nov 2024.
    17. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    18. Masud Alam, 2024. "Volatility in U.S. Housing Sector and the REIT Equity Return," The Journal of Real Estate Finance and Economics, Springer, vol. 69(3), pages 505-544, October.
    19. Romain Houssa & Lasse Bork & Hans Dewachter, 2008. "Identification of Macroeconomic Factors in Large Panels," Working Papers 1010, University of Namur, Department of Economics.
    20. Eickmeier, Sandra & Hofmann, Boris, 2013. "Monetary Policy, Housing Booms, And Financial (Im)Balances," Macroeconomic Dynamics, Cambridge University Press, vol. 17(4), pages 830-860, June.

    More about this item

    Keywords

    FAVAR; regresión por cuantiles; coodependencia; CoV aR;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G2 - Financial Economics - - Financial Institutions and Services
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdr:temest:062. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Clorith Angélica Bahos-Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.