IDEAS home Printed from https://ideas.repec.org/a/bdr/ensayo/v29y2011i64p62-109.html
   My bibliography  Save this article

Regresión del cuantil aplicada al modelo de redes neuronales artificiales

Author

Listed:
  • Charle Augusto Londoño

Abstract

Existen diversas metodologías para calcular el valor en riesgo (VaR) que pretenden capturar principalmente el riesgo de mercado al que están expuestas las instituciones financieras. Siendo el modelo de valor en riesgo condicional autorregresivo (CAViaR) de Engle y Manganelli (1999, 2001, 2004) una buena aproximación empírica para la verdadera medida VaR, tanto para cubrir el riesgo como para el cumplimiento de la regulación bancaria. Por consiguiente, el objetivo de este artículo es realizar una aproximación al modelo CAViaR para el mercado de valores colombiano, empleando diferentes factores de riesgo macroeconómicos y financieros como los esbozados en Chernozhukov y Umantsev (2001); además, se busca establecer qué regla empírica permite una mejor captura del comportamiento del índice general de la Bolsa de Valores de Colombia (IGBC).

Suggested Citation

  • Charle Augusto Londoño, 2011. "Regresión del cuantil aplicada al modelo de redes neuronales artificiales," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(64), pages 62-109, July.
    • Handle: RePEc:bdr:ensayo:v:29:y:2011:i:64:p:62-109
    DOI: 10.32468/Espe.6403
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/Espe.6403
    Download Restriction: no
    File URL: https://libkey.io/10.32468/Espe.6403?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hennie Van Greuning & Sonja Brajovic Bratanovic, . "Analyzing Banking Risk : A Framework for Assessing Corporate Governance and Risk Management, Third Edition," World Bank Publications, The World Bank, number 2618, September.
    2. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    3. Munir A. Jalil B. & Martha Misas A., 2007. "Evaluación de pronósticos del tipo de cambio utilizando redes neuronales y funciones de pérdida asimétricas," Monetaria, CEMLA, vol. 0(3), pages 219-241, julio-sep.
    4. Connor, Gregory & Linton, Oliver, 2007. "Semiparametric estimation of a characteristic-based factor model of common stock returns," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 694-717, December.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Luis Fernando Melo Velandia & Oscar reinaldo Becerra Camargo, 2005. "Medidas de Riesgo, Características y Técnicas de Medición: Una Aplicación del VAR y el ES a la Tasa Interbancaria de Colombia," Borradores de Economia 343, Banco de la Republica de Colombia.
    7. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    8. Gourieroux, C. & Jasiak, J., 2008. "Dynamic quantile models," Journal of Econometrics, Elsevier, vol. 147(1), pages 198-205, November.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. Connor, Gregory, 1984. "A unified beta pricing theory," Journal of Economic Theory, Elsevier, vol. 34(1), pages 13-31, October.
    11. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    12. Manganelli, Simone & White, Halbert & Kim, Tae-Hwan, 2008. "Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR," Working Paper Series 957, European Central Bank.
    13. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    14. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
    15. George Koutoulas & Lawrence Kryzanowski, 1994. "Integration or Segmentation of the Canadian Stock Market: Evidence Based on the APT," Canadian Journal of Economics, Canadian Economics Association, vol. 27(2), pages 329-351, May.
    16. Robert J. Shiller, 2003. "From Efficient Markets Theory to Behavioral Finance," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 83-104, Winter.
    17. 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.
    18. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    19. Solnik, Bruno H., 1974. "An International Market Model of Security Price Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(4), pages 537-554, September.
    20. Robert F. Engle & Simone Manganelli, 1999. "CAViaR: Conditional Value at Risk by Quantile Regression," NBER Working Papers 7341, National Bureau of Economic Research, Inc.
    21. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mauricio Lopera & Ramón Javier Mesa & Charle Londoño, 2014. "Evaluando las intervenciones cambiarias en Colombia: 2004-2012," Estudios Gerenciales, Universidad Icesi, March.
    2. Mauricio Lopera Castano & Ramón Javier Mesa Callejas & Sergio Iván Restrepo Ochoa & Charle Augusto Londono Henao, 2013. "Modelando el esquema de intervenciones del tipo de cambio para Colombia. una aplicación empírica de la técnica de regresión del cuantil bajo redes neu," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, May.

    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. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    2. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    3. Hubner, Stefan, 2016. "Topics in nonparametric identification and estimation," Other publications TiSEM 08fce56b-3193-46e0-871b-0, Tilburg University, School of Economics and Management.
    4. Nieto, María Rosa, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    6. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    7. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    8. Wu, Qi & Yan, Xing, 2019. "Capturing deep tail risk via sequential learning of quantile dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    9. Şener, Emrah & Baronyan, Sayad & Ali Mengütürk, Levent, 2012. "Ranking the predictive performances of value-at-risk estimation methods," International Journal of Forecasting, Elsevier, vol. 28(4), pages 849-873.
    10. Peng, Wei & Hu, Shichao & Chen, Wang & Zeng, Yu-feng & Yang, Lu, 2019. "Modeling the joint dynamic value at risk of the volatility index, oil price, and exchange rate," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 137-149.
    11. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 75, European Central Bank.
    12. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
    13. Shige Peng & Shuzhen Yang & Jianfeng Yao, 2018. "Improving Value-at-Risk prediction under model uncertainty," Papers 1805.03890, arXiv.org, revised Jun 2020.
    14. Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).
    15. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
    16. David Happersberger & Harald Lohre & Ingmar Nolte, 2020. "Estimating portfolio risk for tail risk protection strategies," European Financial Management, European Financial Management Association, vol. 26(4), pages 1107-1146, September.
    17. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    18. Bernard, Carole & Czado, Claudia, 2015. "Conditional quantiles and tail dependence," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 104-126.
    19. Sunil K. Mohanty & Stein Frydenberg & Petter Osmundsen & Sjur Westgaard & Christian Skjøld, 2023. "Risk factors in stock returns of U.S. oil and gas companies: evidence from quantile regression analysis," Review of Quantitative Finance and Accounting, Springer, vol. 60(2), pages 715-746, February.
    20. Petros Messis & Antonis Alexandridis & Achilleas Zapranis, 2021. "Testing and comparing conditional risk‐return relationship with a new approach in the cross‐sectional framework," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 218-240, January.

    More about this item

    Keywords

    valor en riesgo condicional autorregresivo; regresión del cuantil; redes neuronales artificiales; variables macroeconómicas y financieras; regulacion 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

    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:ensayo:v:29:y:2011:i:64:p:62-109. 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.