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Estimación bayesiana del valor en riesgo: una aplicación para el mercado de valores colombiano

Author

Listed:
  • Charle Augusto Londono
  • Juan Carlos Correa
  • Mauricio Lopera

Abstract

Esta investigación tiene como propósito implementar la metodología de regresión cuantil bayesiana en el cálculo del valor en riesgo (VaR, en inglés) en el mercado de valores colombiano. Para este objetivo se valoran algunos requerimientos regulatorios sobre riesgo de mercado definidos por la Superintendencia Financiera de Colombia sobre metodologías, medidas de desempeno y factores de riesgo para el cálculo del VaR, y se compara con el modelo APARCH y de regresión cuantil tradicional; se halla que la regresión cuantil tiene una mejor capacidad para adaptarse a los patrones exhibidos por un portafolio de acciones colombianas dadas varias medidas de desempeno. ***** The purpose of this research is to implement the Bayesian quantile regression methodology in the estimation of the Value at Risk (VaR), in the Colombian stock market. For this objective, some regulatory requirements on market risk are compared using the APARCH model, and traditional quantile regressions. Colombia’s Financial Superintendence defines these requirements based on where they address methodologies, performance measures, and risk factors relevant to the calculation of the VaR. We found out that the latter technique has a greater capacity to adapt to the patterns exhibited by a portfolio of Colombian stock given several performance measures.

Suggested Citation

  • Charle Augusto Londono & Juan Carlos Correa & Mauricio Lopera, 2014. "Estimación bayesiana del valor en riesgo: una aplicación para el mercado de valores colombiano," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, August.
    • Handle: RePEc:col:000093:012151
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    References listed on IDEAS

    as
    1. Cai, Zongwu & Wang, Xian, 2008. "Nonparametric estimation of conditional VaR and expected shortfall," Journal of Econometrics, Elsevier, vol. 147(1), pages 120-130, November.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Luis Melo Velandia & Luis Fernando Melo Velandia, 2019. "Regresión cuantílica dinámica para la medición del valor en riesgo: Una aplicación a datos colombianos," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 38(76), pages 23-50, January.

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    More about this item

    Keywords

    valor en riesgo condicional autorregresivo; regresión cuantil; estadística bayesiana; variables macroeconómicas y financieras; regulación bancaria; mercado de valores.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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