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Modeling and Forecasting the Dynamics in Romanian Stock Market Indices Using Threshold Models

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  • Acatrinei, Marius Cristian

    (National Institute for Economic Research, Romanian Academy)

  • Caraiani, Petre

    (Institute for Economic Forecasting, Romanian Academy)

Abstract

We investigate the existence of nonlinear patterns in the dynamics of the main stock index returns in Romania. We use daily closing data of the BET stock index series from 2004 to early 2010. Based on several tests for nonlinearity we reject the null hypothesis of linearity. We use several types of threshold models and compare their fitness and forecasting performance with basic AR models. We found that the LSTAR and SETAR models fit best the data; however, they cannot outperform the simpler AR models in forecasting. These results suggest that although there are nonlinear features in data, the threshold models are not complex enough to reveal the data complexity.

Suggested Citation

  • Acatrinei, Marius Cristian & Caraiani, Petre, 2011. "Modeling and Forecasting the Dynamics in Romanian Stock Market Indices Using Threshold Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 42-54, June.
  • Handle: RePEc:rjr:romjef:v::y:2011:i:2:p:42-54
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    References listed on IDEAS

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    1. 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.
    2. Hinich Melvin J & Mendes Eduardo M & Stone Lewi, 2005. "Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-15, December.
    3. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    4. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    5. Abhyankar, A & Copeland, L S & Wong, W, 1995. "Nonlinear Dynamics in Real-Time Equity Market Indices: Evidence from the United Kingdom," Economic Journal, Royal Economic Society, vol. 105(431), pages 864-880, July.
    6. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    7. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
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    Cited by:

    1. Andrei ANGHEL & Dalina DUMITRESCU & Cristiana TUDOR, 2015. "Modeling Portfolio Returns On Bucharest Stock Exchange Using The Fama-French Multifactor Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 22-46, March.

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    More about this item

    Keywords

    Nonlinear Models; Forecasting Models; Threshold Autoregression; Smooth Transition Autoregression; Simulation Techniques;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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