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Caracterización no lineal y predicción no paramétrica en el IBEX35/Nonlinear Characterization and Predictions of IBEX 35

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  • OLMEDO,E.

    () (Departamento de Economía Aplicada I. Universidad de Sevilla. Avda. Ramón y Cajal, nº 1. 41018 Sevilla. Spain.)

  • VELASCO, F.

    () (Departamento de Economía Aplicada I. Universidad de Sevilla. Avda. Ramón y Cajal, nº 1. 41018 Sevilla. Spain.)

  • VALDERAS, J.M.

    () (Departamento de Economía Aplicada I. Universidad de Sevilla. Avda. Ramón y Cajal, nº 1. 41018 Sevilla. Spain.)

Abstract

The nonlinear modelization has experimented a great resurgence of the hand of Chaos Theory, which shown the possibility of obtaining complex behaviors produced endogenously by the dynamics of the model, without the necessity to include exogenous random shocks. On the other hand, the importance of the forecasting ability in Economics is principal. In the work, different techniques developed within Complex Econometrics are applied to improve forecasting in stock markets. La modelización no lineal ha experimentado un resurgimiento de la mano de la teoría del caos, que ha puesto de manifiesto la posibilidad de conseguir comportamientos complejos producidos por la dinámica endógena del modelo, sin la necesidad de incluir perturbaciones aleatorias exógenas al mismo. Por otro lado, es indiscutible la importancia de la capacidad de predicción en economía. En el presente trabajo se aplican diferentes técnicas desarrolladas dentro de lo que se denomina Econometría Compleja No Lineal buscando mejorar la capacidad de predicción en los mercados financieros.

Suggested Citation

  • Olmedo,E. & Velasco, F. & Valderas, J.M., 2007. "Caracterización no lineal y predicción no paramétrica en el IBEX35/Nonlinear Characterization and Predictions of IBEX 35," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 25, pages 815-842, Diciembre.
  • Handle: RePEc:lrk:eeaart:25_3_11
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    as
    1. Day, Richard H. & Huang, Weihong, 1990. "Bulls, bears and market sheep," Journal of Economic Behavior & Organization, Elsevier, vol. 14(3), pages 299-329, December.
    2. Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
    3. Shintani, Mototsugu & Linton, Oliver, 2004. "Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos," Journal of Econometrics, Elsevier, pages 1-33.
    4. Bajo-Rubio, Oscar & Fernandez-Rodriguez, Fernando & Sosvilla-Rivero, Simon, 1992. "Chaotic behaviour in exchange-rate series : First results for the Peseta--U.S. dollar case," Economics Letters, Elsevier, vol. 39(2), pages 207-211, June.
    5. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    6. Turner, C.M. & Startz, R. & Nelson, C.R., 1989. "The Markov Model Of Heteroskedasticity, Risk And Learning In The Stock Market," Working Papers 89-01, University of Washington, Department of Economics.
    7. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-337, July.
    8. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, pages 315-332.
    9. Jorge Perez-Rodriguez & Salvador Torra & Julian Andrada-Felix, 2005. "Are Spanish Ibex35 stock future index returns forecasted with non-linear models?," Applied Financial Economics, Taylor & Francis Journals, pages 963-975.
    10. Lux, Thomas, 1997. "Time variation of second moments from a noise trader/infection model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(1), pages 1-38, November.
    11. Barnett, William A. & Gallant, A. Ronald & Hinich, Melvin J. & Jungeilges, Jochen A. & Kaplan, Daniel T. & Jensen, Mark J., 1997. "A single-blind controlled competition among tests for nonlinearity and chaos," Journal of Econometrics, Elsevier, pages 157-192.
    12. Beine, Michel & Benassy-Quere, Agnes & Lecourt, Christelle, 2002. "Central bank intervention and foreign exchange rates: new evidence from FIGARCH estimations," Journal of International Money and Finance, Elsevier, vol. 21(1), pages 115-144, February.
    13. David G. McMillan, 2003. "Non-linear Predictability of UK Stock Market Returns," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(5), pages 557-573, December.
    14. Hsieh, David A, 1991. " Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    15. Turner, Christopher M. & Startz, Richard & Nelson, Charles R., 1989. "A Markov model of heteroskedasticity, risk, and learning in the stock market," Journal of Financial Economics, Elsevier, vol. 25(1), pages 3-22, November.
    16. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
    17. Richard T. Baillie & Tim Bollerslev, 1991. "Intra-Day and Inter-Market Volatility in Foreign Exchange Rates," Review of Economic Studies, Oxford University Press, vol. 58(3), pages 565-585.
    18. Claudio Bonilla & Rafael Romero-Meza & Melvin Hinich, 2006. "Episodic nonlinearity in Latin American stock market indices," Applied Economics Letters, Taylor & Francis Journals, vol. 13(3), pages 195-199.
    19. Abhyankar, A & Copeland, L S & Wong, W, 1997. "Uncovering Nonlinear Structure in Real-Time Stock-Market Indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 1-14, January.
    20. Brooks, Chris, 2001. "A Double-Threshold GARCH Model for the French Franc/Deutschmark Exchange Rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 135-143, March.
    21. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, pages 926-947.
    22. Soofi, Abdol S. & Cao, Liangyue, 1999. "Nonlinear deterministic forecasting of daily Peseta-Dollar exchange rate," Economics Letters, Elsevier, vol. 62(2), pages 175-180, February.
    23. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, pages 169-183.
    24. Hinich, Melvin J & Patterson, Douglas M, 1985. "Evidence of Nonlinearity in Daily Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(1), pages 69-77, January.
    25. Bask, Mikael, 2000. "A Positive Lyapunov Exponent in Swedish Exchange Rates?," Umeå Economic Studies 528, Umeå University, Department of Economics.
    26. Hyun J. Jin & Darren L. Frechette, 2004. "Fractional Integration in Agricultural Futures Price Volatilities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 432-443.
    27. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    28. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, pages 315-332.
    29. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
    30. Farmer, J. Doyne & Joshi, Shareen, 2002. "The price dynamics of common trading strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 149-171, October.
    31. Bradley, Michael D. & Jansen, Dennis W., 2004. "Forecasting with a nonlinear dynamic model of stock returns and industrial production," International Journal of Forecasting, Elsevier, vol. 20(2), pages 321-342.
    32. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    33. Fernandez-Rodriguez, Fernando & Sosvilla-Rivero, Simon, 1998. "Testing nonlinear forecastability in time series: Theory and evidence from the EMS," Economics Letters, Elsevier, vol. 59(1), pages 49-63, April.
    34. Lisi, Francesco & Medio, Alfredo, 1997. "Is a random walk the best exchange rate predictor?," International Journal of Forecasting, Elsevier, vol. 13(2), pages 255-267, June.
    35. Cao, Liangyue & Soofi, Abdol S., 1999. "Nonlinear deterministic forecasting of daily dollar exchange rates," International Journal of Forecasting, Elsevier, vol. 15(4), pages 421-430, October.
    36. Jorge Belaire-Franch, & Dulce Contreras & Lorena Tordera-Lledo, 2002. "Assessing Non-Linear Structures in Real Exchange Rates Using Recurrence Plot Strategies," Computing in Economics and Finance 2002 239, Society for Computational Economics.
    37. Carl Chiarella, 1992. "The Dynamics of Speculative Behaviour," Working Paper Series 13, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    38. Ercan Balaban & Aslı Bayar, 2005. "Stock returns and volatility: empirical evidence from fourteen countries," Applied Economics Letters, Taylor & Francis Journals, vol. 12(10), pages 603-611.
    39. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-368, July.
    40. Agnon, Yehuda & Golan, Amos & Shearer, Matthew, 1999. "Nonparametric, nonlinear, short-term forecasting: theory and evidence for nonlinearities in the commodity markets," Economics Letters, Elsevier, vol. 65(3), pages 293-299, December.
    41. Weixian Wei, 2002. "Forecasting stock market volatility with non-linear GARCH models: a case for China," Applied Economics Letters, Taylor & Francis Journals, vol. 9(3), pages 163-166.
    42. Sarantis, Nicholas, 2001. "Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence," International Journal of Forecasting, Elsevier, vol. 17(3), pages 459-482.
    43. Cecen, A. Aydin & Erkal, Cahit, 1996. "Distinguishing between stochastic and deterministic behavior in high frequency foreign exchange rate returns: Can non-linear dynamics help forecasting?," International Journal of Forecasting, Elsevier, vol. 12(4), pages 465-473, December.
    44. Chen, Shu-Heng & Lux, Thomas & Marchesi, Michele, 2001. "Testing for non-linear structure in an artificial financial market," Journal of Economic Behavior & Organization, Elsevier, vol. 46(3), pages 327-342, November.
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    Keywords

    No linealidad; Predicción; Contraste BDS; Contraste de Kaplan; Modelos ARIMA; Vecinos próximos; Redes neuronales.;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • 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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