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Redes neuronales para predecir el tipo de cambio diario

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  • Barrera, Carlos R.

    (Banco Central de Reserva del Perú)

Abstract

Un problema recurrente es que los modelos estructurales de determinación del tipo de cambio no logran predecirlo con mayor precisión que un camino aleatorio. El objetivo de la presente investigación es verificar si es posible obtener proyecciones relativamente precisas generadas por un grupo de modelos econométricos para el tipo de cambio diario sobre la base de la muestra disponible enero 2004 - setiembre 2008. Los modelos a compararse en términos predictivos son: (a) camino aleatorio en el nivel del tipo de cambio; (b) auto-regresión con p rezagos en la variación del tipo de cambio; (c) perceptrones con p rezagos en la variación del tipo de cambio y (d) auto-regresión fraccional con p rezagos en el nivel del tipo de cambio. Los resultados obtenidos confirman que los perceptrones poseen la capacidad para anticipar el patrón de los movimientos diarios en el tipo de cambio, especialmente cuando se utiliza el spread entre el tipo de cambio venta y compra como porcentaje del tipo de cambio promedio de estas dos cotizaciones, la depreciación diaria del yen contra el dólar americano y el diferencial de tasas domésticas de interés interbancarias en ambas monedas.

Suggested Citation

  • Barrera, Carlos R., 2010. "Redes neuronales para predecir el tipo de cambio diario," Working Papers 2010-001, Banco Central de Reserva del Perú.
    • Handle: RePEc:rbp:wpaper:2010-001
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    as
    1. Rech, Gianluigi & Teräsvirta, Timo & Tschernig, Rolf, 1999. "A simple variable selection technique for nonlinear models," SSE/EFI Working Paper Series in Economics and Finance 296, Stockholm School of Economics, revised 06 Apr 2000.
    2. Martin D.D. Evans & Richard K. Lyons, 2017. "Order Flow and Exchange Rate Dynamics," World Scientific Book Chapters, in: Studies in Foreign Exchange Economics, chapter 6, pages 247-290, World Scientific Publishing Co. Pte. Ltd..
    3. Pollock, Andrew C. & Macaulay, Alex & Thomson, Mary E. & Onkal, Dilek, 2005. "Performance evaluation of judgemental directional exchange rate predictions," International Journal of Forecasting, Elsevier, vol. 21(3), pages 473-489.
    4. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    5. Jan J. J. Groen, 1999. "Long horizon predictability of exchange rates: Is it for real?," Empirical Economics, Springer, vol. 24(3), pages 451-469.
    6. Charles Bond & Ken Richardson, 2004. "Seeing the FisherZ-transformation," Psychometrika, Springer;The Psychometric Society, vol. 69(2), pages 291-303, June.
    7. Yang, Kun & Shintani, Mototsugu, 2006. "Does the prediction horizon matter for the forward premium anomaly? Evidence from panel data," Economics Letters, Elsevier, vol. 93(2), pages 255-260, November.
    8. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Chung, Ching-Fan & Baillie, Richard T, 1993. "Small Sample Bias in Conditional Sum-of-Squares Estimators of Fractionally Integrated ARMA Models," Empirical Economics, Springer, vol. 18(4), pages 791-806.
    11. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    12. Manabu Asai & Michael McAleer, 2007. "Non-trading day effects in asymmetric conditional and stochastic volatility models," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 113-123, March.
    13. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    14. Philippe Bacchetta & Eric Van Wincoop, 2004. "A Scapegoat Model of Exchange-Rate Fluctuations," American Economic Review, American Economic Association, vol. 94(2), pages 114-118, May.
    15. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    16. MacDonald, Ronald & Taylor, Mark P., 1994. "The monetary model of the exchange rate: long-run relationships, short-run dynamics and how to beat a random walk," Journal of International Money and Finance, Elsevier, vol. 13(3), pages 276-290, June.
    17. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-218, March.
    18. Olgun, Hasan & Ozdemir, Zeynel Abidin, 2008. "Linkages between the center and periphery stock prices: Evidence from the vector ARFIMA model," Economic Modelling, Elsevier, vol. 25(3), pages 512-519, May.
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    Cited by:

    1. Carlos R. Barrera Chaupis, 2018. "Inventory Adjustments to Demand Shocks under Flexible Specifications," Monetaria, Centro de Estudios Monetarios Latinoamericanos, CEMLA, vol. 0(1), pages 149-201, january-j.
    2. Barrera, Carlos R., 2011. "Impacto amplificador del ajuste de inventarios ante choques de demanda según especificaciones flexibles," Working Papers 2011-009, Banco Central de Reserva del Perú.

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    More about this item

    Keywords

    Auge crediticio; política monetaria;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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