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Relación entre los valores en riesgo de los principales mercados financieros colombianos: un enfoque a través de modelos multivariados de regresión cuantílica

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Abstract

En este documento se estima el valor en riesgo a partir de un modelo multivariado de regresión cuantílica. Este tipo de modelos permiten capturar hechos estilizados de las series financieras y evitan imponer supuestos relacionados con la distribución de estas variables. A diferencia de las metodologías usuales de enfoque univariado, ésta toma en cuenta interrelaciones con riesgos de mercado de otras variables. Adicionalmente, este tipo de modelos permite calcular funciones de pseudo impulso-respuesta. Este modelo se estimó sobre el índice de mercado bursátil de la bolsa de valores (COLCAP), la tasa de cambio con respecto al dólar (TRM) y un índice de precios de títulos de deuda pública (IDXTES) para la muestra comprendida entre el periodo 04/Ene/2008 y 23/Nov/2015. Al comparar el VaR obtenido por este modelo con técnicas tradicionales, se encontró que las medidas de riesgo de mercado bajo esta metodología tienen un buen desempeño. Adicionalmente, las funciones de pseudo impulso-respuesta indican que los choques generados en las variables TRM e IDXTES presentan respuestas negativas y persistentes en sus propios valores en riesgo. Por otro lado, los mayores impactos cruzados en los valores en riesgo se presentan en las series TRM y COLCAP ante choques en IDXTES; y en IDXTES ante choques en TRM.

Suggested Citation

  • Daniel Mariño-Ustacara & Luis Fernando Melo-Velandia, 2016. "Relación entre los valores en riesgo de los principales mercados financieros colombianos: un enfoque a través de modelos multivariados de regresión cuantílica," Borradores de Economia 975, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:975
    DOI: 10.32468/be.975
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    References listed on IDEAS

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    1. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
    2. José E. Gómez-González & Luis Fernando Melo Velandia, 2014. "Efectos de «ángeles caídos» en el mercado accionario colombiano: estudio de eventos del caso Interbolsa," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 32(75), pages 23-27, December.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Loaiza-Maya, Rubén Albeiro & Gómez-González, José Eduardo & Melo-Velandia, Luis Fernando, 2015. "Exchange rate contagion in Latin America," Research in International Business and Finance, Elsevier, vol. 34(C), pages 355-367.
    5. Espinosa-Torres, Juan Andrés & Gomez-Gonzalez, Jose Eduardo & Melo-Velandia, Luis Fernando & Moreno-Gutiérrez, José Fernando, 2016. "The international transmission of risk: Causal relations among developed and emerging countries’ term premia," Research in International Business and Finance, Elsevier, vol. 37(C), pages 646-654.
    6. Alejandro Reveiz Herault & Carlos Eduardo León Rincón, 2008. "Índice representativo del mercado de deuda pública interna: IDXTES," Borradores de Economia 4522, Banco de la Republica.
    7. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    8. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.
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    More about this item

    Keywords

    Valor en riesgo; regresión cuantílica multivariada; procesos CAViaR; funciones de pseudo impulso-respuesta.;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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