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Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns

Author

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  • Eleni Constantinou

    (Department of Accounting and Finance, The Philips College, Cyprus)

  • Robert Georgiades

    (Department of Accounting and Finance, The Philips College, Cyprus)

  • Avo Kazandjian

    (Department of Business Studies, The Philips College, Cyprus)

  • Georgios P. Kouretas

    (Department of Economics, University of Crete, Greece)

Abstract

This paper provides an analysis of regime switching in volatility and out-of-sample forecasting of the Cyprus Stock Exchange by using daily data for the period 1996-2002. We first model volatility regime switching within a univariate Markov switching framework. Modelling stock returns within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. The results show that linearity is rejected in favour of an MS specification, which forms statistically an adequate representation of the data. Two regimes are implied by the model, the high-volatility regime and the low-volatility one, and they provide quite accurately the state of volatility associated with the presence of a rational bubble in the capital market of Cyprus. Another implication is that there is evidence of regime clustering. We then provide out-of-sample forecasts of the CSE daily returns by using two competing nonlinear models, the univariate Markov switching model and the Artificial Neural Network Model. The comparison of the out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test and forecast encompassing, using the Clements and Hendry test. The results suggest that both nonlinear models are equivalent in forecasting accuracy and forecasting encompassing, and therefore on forecasting performance. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Eleni Constantinou & Robert Georgiades & Avo Kazandjian & Georgios P. Kouretas, 2006. "Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 371-383.
  • Handle: RePEc:ijf:ijfiec:v:11:y:2006:i:4:p:371-383 DOI: 10.1002/ijfe.305
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    References listed on IDEAS

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    1. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.

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