IDEAS home Printed from https://ideas.repec.org/a/elt/journl/v81y2014i323p721-746.html
   My bibliography  Save this article

La cópula GED bivariada. Una aplicación en entornos de crisis

Author

Listed:
  • Mendoza, Alfonso.

    (Universidad Popular Autónoma del Estado de Puebla.)

  • Galvanovskis, Evalds.

    (Instituto Nacional de Estadística y Geografía.)

Abstract

The General Error Distribution (GED) has been extensively used in time series econometrics applications, due to its great flexibility in the estimation of financial stylized facts. However, there has been no attempt to employ this statistical distribution in the construction of copulas. Copulas are probability functions that link one multivariate distribution to univariate distribution functions called marginals. These marginals are continuous and follow a uniform behavior within [0,1]. In this paper we introduce the bivariate GED copula to investigate financial contagion in Latinamerica during the 2008 crisis. We examine contagion in foreign exchange, equity, bonds and sovereign markets in Latinamerica. Standard decision criteria provide strong evidence in favor of the GED copula, against other widely used elliptical and arquimidean alternatives.// Mientras que la distribución de error general (GED) ha sido usada extensamente en aplicaciones de series de tiempo y ha demostrado una gran flexibilidad en la estimación de series de tiempo financieras, no se ha intentado utilizarla en la construcción de cópulas. Las cópulas son funciones de probabilidad que unen una función de distribución multivariada a funciones de distribución univariadas llamadas marginales. Se parte del supuesto de que las marginales son continuas y uniformes en el intervalo [0,1]. En este artículo proponemos la cópula GED bivariada, la cual, de acuerdo con nuestra revisión, no ha sido usada en la bibliografía. Esta función abarca otras funciones de distribución, como la gausiana o la doble exponencial, empleadas frecuentemente en el análisis de fenómenos financieros. Con el fin de probar el desempeño de esta nueva cópula investigamos el contagio financiero en la crisis de 2008 empleando tipos de cambio, acciones, bonos y mercados de deuda soberana en América Latina. Los criterios usuales de decisión proveen fuerte evidencia a favor de la cópula GED sobre otras alternativas elípticas o arquimideanas.

Suggested Citation

  • Mendoza, Alfonso. & Galvanovskis, Evalds., 2014. "La cópula GED bivariada. Una aplicación en entornos de crisis," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(323), pages .721-746, julio-sep.
  • Handle: RePEc:elt:journl:v:81:y:2014:i:323:p:721-746
    DOI: http://dx.doi.org/10.20430/ete.v81i323.128
    as

    Download full text from publisher

    File URL: http://www.eltrimestreeconomico.com.mx/index.php/te/article/view/128/128
    Download Restriction: no

    File URL: https://libkey.io/http://dx.doi.org/10.20430/ete.v81i323.128?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. Hyung, Namwon & de Vries, Casper G., 2007. "Portfolio selection with heavy tails," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 383-400, June.
    2. Ivana Komunjer, 2007. "Asymmetric power distribution: Theory and applications to risk measurement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 891-921.
    3. Salmon, Mark & Schleicher, Christoph & Hurd, Matthew, 2005. "Using Copulas to Construct Bivariate Foreign Exchange Distributions with an Application to the Sterling Exchange Rate Index," CEPR Discussion Papers 5114, C.E.P.R. Discussion Papers.
    4. Roman Liesenfeld & Robert C. Jung, 2000. "Stochastic volatility models: conditional normality versus heavy-tailed distributions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 137-160.
    5. Lorán Chollete & Andréas Heinen & Alfonso Valdesogo, 2009. "Modeling International Financial Returns with a Multivariate Regime-switching Copula," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 437-480, Fall.
    6. Lucchetti, Riccardo, 2002. "Analytical Score for Multivariate GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 19(2), pages 133-143, April.
    7. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    8. Hsieh, David A, 1989. "Modeling Heteroscedasticity in Daily Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(3), pages 307-317, July.
    9. Joe, Harry & Hu, Taizhong, 1996. "Multivariate Distributions from Mixtures of Max-Infinitely Divisible Distributions," Journal of Multivariate Analysis, Elsevier, vol. 57(2), pages 240-265, May.
    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. Mendoza-Velázquez, Alfonso & Galvanovskis, Evalds, 2009. "Introducing the GED-Copula with an application to Financial Contagion in Latin America," MPRA Paper 46669, University Library of Munich, Germany, revised 01 Feb 2010.
    2. Koliai, Lyes, 2016. "Extreme risk modeling: An EVT–pair-copulas approach for financial stress tests," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 1-22.
    3. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    4. Li, Xiao-Ming & Rose, Lawrence C., 2009. "The tail risk of emerging stock markets," Emerging Markets Review, Elsevier, vol. 10(4), pages 242-256, December.
    5. Tachibana, Minoru, 2022. "Safe haven assets for international stock markets: A regime-switching factor copula approach," Research in International Business and Finance, Elsevier, vol. 60(C).
    6. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    7. Dimic, Nebojsa & Piljak, Vanja & Swinkels, Laurens & Vulanovic, Milos, 2021. "The structure and degree of dependence in government bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    8. Colavecchio, Roberta & Funke, Michael, 2008. "Volatility transmissions between renminbi and Asia-Pacific on-shore and off-shore U.S. dollar futures," China Economic Review, Elsevier, vol. 19(4), pages 635-648, December.
    9. Mahdi Teimouri & Saralees Nadarajah, 2022. "Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 665-692, August.
    10. DiTraglia, Francis J. & Gerlach, Jeffrey R., 2013. "Portfolio selection: An extreme value approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 305-323.
    11. de Carvalho, Pablo Jose Campos & Gupta, Aparna, 2018. "A network approach to unravel asset price comovement using minimal dependence structure," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 119-132.
    12. Zhu, Dongming & Zinde-Walsh, Victoria, 2009. "Properties and estimation of asymmetric exponential power distribution," Journal of Econometrics, Elsevier, vol. 148(1), pages 86-99, January.
    13. Christoffersen, Peter & Langlois, Hugues, 2013. "The Joint Dynamics of Equity Market Factors," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(5), pages 1371-1404, October.
    14. repec:zbw:bofitp:2006_016 is not listed on IDEAS
    15. Henryk Gurgul & Robert Syrek, 2010. "Polish stock market and some foreign markets - dependence analysis by regime-switching copulas," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 8, pages 21-39.
    16. Chollete, Loran & Ismailescu, Iuliana & Lu, Ching-Chih, 2014. "Dependence between Extreme Events in the Real and Financial Sectors," UiS Working Papers in Economics and Finance 2014/12, University of Stavanger.
    17. Chang, Kuang-Liang, 2023. "The low-magnitude and high-magnitude asymmetries in tail dependence structures in international equity markets and the role of bilateral exchange rate," Journal of International Money and Finance, Elsevier, vol. 133(C).
    18. Zhichao Zhang & Li Ding & Fan Zhang & Zhuang Zhang, 2015. "Optimal Currency Composition for China's Foreign Reserves: A Copula Approach," The World Economy, Wiley Blackwell, vol. 38(12), pages 1947-1965, December.
    19. Tong, Bin & Diao, Xundi & Wu, Chongfeng, 2015. "Modeling asymmetric and dynamic dependence of overnight and daytime returns: An empirical evidence from China Banking Sector," Economic Modelling, Elsevier, vol. 51(C), pages 366-382.
    20. Allen, Linda & Bali, Turan G., 2007. "Cyclicality in catastrophic and operational risk measurements," Journal of Banking & Finance, Elsevier, vol. 31(4), pages 1191-1235, April.
    21. Garcia, René & Tsafack, Georges, 2011. "Dependence structure and extreme comovements in international equity and bond markets," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1954-1970, August.

    More about this item

    Keywords

    distribución GED; cópula; distribución bivariada; contagio; mercados financieros; GARCH.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

    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:elt:journl:v:81:y:2014:i:323:p:721-746. 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: Nuria Pliego Vinageras (email available below). General contact details of provider: http://www.fondodeculturaeconomica.com/ .

    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.