Modeling and Forecasting the Dynamics in Romanian Stock Market Indices Using Threshold Models
AbstractWe 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.
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Bibliographic InfoArticle provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.
Volume (Year): (2011)
Issue (Month): 2 (June)
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Nonlinear Models; Forecasting Models; Threshold Autoregression; Smooth Transition Autoregression; Simulation Techniques;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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- Terasvirta, Timo, 2006.
"Forecasting economic variables with nonlinear models,"
Handbook of Economic Forecasting,
- Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
- 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.
- 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.
- James M. Poterba & Lawrence H. Summers, 1987. "Mean Reversion in Stock Prices: Evidence and Implications," NBER Working Papers 2343, National Bureau of Economic Research, Inc.
- Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-96, July.
- repec:att:wimass:9621 is not listed on IDEAS
- Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-37, July.
- 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.
- 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 864-80, July.
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