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

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Author Info
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)

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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.

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File URL: http://hdl.handle.net/10.1002/ijfe.305
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal International Journal of Finance & Economics.

Volume (Year): 11 (2006)
Issue (Month): 4 ()
Pages: 371-383
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Handle: RePEc:ijf:ijfiec:v:11:y:2006:i:4:p:371-383

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  1. Engel, Charles & Hamilton, James D, 1990. "Long Swings in the Dollar: Are They in the Data and Do Markets Know It?," American Economic Review, American Economic Association, vol. 80(4), pages 689-713, September. [Downloadable!] (restricted)
  2. Campbell, John Y. & Lo, Andrew W. & MacKinlay, A. Craig & Whitelaw, Robert F., 1998. "The Econometrics Of Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(04), pages 559-562, December. [Downloadable!]
  3. Kanas, Angelos, 2001. "Neural Network Linear Forecasts for Stock Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 245-54, July. [Downloadable!] (restricted)
  4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March. [Downloadable!] (restricted)
  5. Engel, Charles, 1994. "Can the Markov switching model forecast exchange rates?," Journal of International Economics, Elsevier, vol. 36(1-2), pages 151-165, February. [Downloadable!] (restricted)
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  6. James D. Hamilton & Gang Lin, 1996. "Stock Market Volatility and The Business Cycle," University of California at San Diego, Economics Working Paper Series 96-18, Department of Economics, UC San Diego. [Downloadable!]
    Other versions:
  7. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January. [Downloadable!] (restricted)
  8. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-36, July. [Downloadable!] (restricted)
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  9. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November. [Downloadable!] (restricted)
    Other versions:
  10. Kanas, Angelos, 2005. "Regime linkages between the Mexican currency market and emerging equity markets," Economic Modelling, Elsevier, vol. 22(1), pages 109-125, January. [Downloadable!] (restricted)
  11. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January. [Downloadable!] (restricted)
  12. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-75, July.
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  13. Kanas, Angelos & Kouretas, Georgios P., 2007. "Regime dependence between the official and parallel foreign currency markets for US dollars in Greece," Journal of Macroeconomics, Elsevier, vol. 29(2), pages 431-449, June. [Downloadable!] (restricted)
  14. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333. [Downloadable!] (restricted)
    Other versions:
  15. Elliott, Graham, 1999. "Efficient Tests for a Unit Root When the Initial Observation Is Drawn from Its Unconditional Distribution," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(3), pages 767-83, August.
    Other versions:
  16. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583. [Downloadable!] (restricted)
  17. Coe, Patrick J, 2002. "Financial Crisis and the Great Depression: A Regime Switching Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(1), pages 76-93, February.
  18. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
  19. Kanas, Angelos & Yannopoulos, Andreas, 2001. "Comparing linear and nonlinear forecasts for stock returns," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 383-398, December. [Downloadable!] (restricted)
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