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

International Journal of Finance and Economics

Volumn 11, Issue 4, 2006, Pages 371-383

  Constantinou, Eleni ^a   Georgiades, Robert ^a   Kazandjian, Avo ^a   Kouretas, Georgios P ^b  

^a Philips College   (Cyprus)

^b UNIVERSITY OF CRETE   (Greece)

Author keywords

Artificial neural networks; Forecast encompassing; Forecasting accuracy; Nonlinearity; Regime switching; Stock returns

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CAPITAL MARKET; FORECASTING METHOD; MARKOV CHAIN; STATISTICAL ANALYSIS; STOCK MARKET;

ASIA; CYPRUS; EURASIA; MIDDLE EAST;

EID: 33750316278     PISSN: 10769307     EISSN: 10991158     Source Type: Journal    
DOI: 10.1002/ijfe.305     Document Type: Article

References (34)

1. Adya M, Collopy F. 1998. How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting 17: 481-495.
2. Campbell JY, Lo AW, MacKinlay AC. 1998. The Econometrics of Financial Markets. Princeton University Press: Princeton, NJ.
3. Clemen RT. 1989. Combining forecasts: a review and annotated bibliography. International Journal of Forecasting 5: 671-690.
4. Clements MP, Hendry DF. 1998. Forecasting Economic Time Series. Cambridge University Press: Cambridge, UK.
5. Coe PJ. 2002. Financial crisis and the great depression: a regime switching approach. Journal of Money, Credit and Banking 34: 76-93.
6. Constantinou E, Georgoutsos DA, Kanas A, Kouretas GP, Siakalli K. 2006. Cyprus Stock Exchange: Evaluation, Performance and Prospects of an Emerging Capital Market. Cyprus Promotion Research Foundation: Nicosia, Cyprus, forthcoming.
7. Davies RB. 1987. Hypothesis testing when the nuisance parameter is present only under the alternative. Biometrika 74: 33-43.
8. Dempster MP, Laird NM, Rubin DB. 1977. Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39: 1-38.
9. Diebold FX, Mariano RS. 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13: 253-263.
10. Donaldson RG, Kamstra M. 1996. Forecast combining with neural networks. Journal of Forecasting 15: 49-61.
11. Donaldson RG, Kamstra M. 1997. An artificial neural network - GARCH model for international stock return volatility. Journal of Empirical Finance 4: 17-46.
12. Edwards S, Susmel R. 2001. Volatility dependence and contagion in emerging equities markets. Journal of Development Economics 66: 505-532.
13. Elliott G. 1999. Efficient tests for a unit root when the initial observation is drawn from its unconditional distribution. International Economic Review 44: 767-784.
14. Elliott G. Rothenberg JT, Stock JH. 1996. Efficient tests for an autoregressive unit root. Econometrica 64: 813-836.
15. Engel C. 1994. Can the Markov switching model forecast exchange rates? Journal of International Economics 36: 151-165.
16. Engel C, Hamilton JD. 1990. Long swings in the Dollar: are they in the data and do markets know it? American Economic Review 80: 689-713.
17. Engle C, Kim CJ. 2001. The long-run US/UK real exchange rate. Journal of Money, Credit and Banking 31: 335-356.
18. Hamilton JD. 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57: 357-384.
19. Hamilton JD. 1994. Time Series Analysis. Princeton University Press: Princeton, NJ.
20. Hamilton JD. 1996. Specification tests in Markov-switching time-series models. Journal of Econometrics 70: 127-157.
21. Hamilton JD, Lin G. 1996. Stock market volatility and the business cycle. Journal of Applied Econometrics 11: 573-593.
22. Hamilton JD, Susmel R. 1994. Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics 64: 307-333.
23. Kanas A. 2001. Neural networks linear forecasts for stock returns. International Journal of Finance and Economics 6: 245-254.
24. Kanas A. 2002. Non-linear forecasts of stock returns. Journal of Forecasting 22: 299-315.
25. Kanas A. 2005. Regime linkages between the Mexican currency market and emerging equity markets. Economic Modelling 22: 109-125.
26. Kanas A, Kouretas GP. 2007. Regime dependence between the official and parallel foreign currency markets for Dollars in Greece. Journal of Macroeconomics, forthcoming.
27. Kanas A, Yannopoulos A. 2001. Comparing linear and nonlinear forecasts for stock returns. International Review of Economics and Finance 10: 383-398.
28. Kavalieris L. 1989. The estimation of the order of an autoregression using recursive residuals and crossvalidation. Journal of Time Series Analysis 10: 178-271.
29. Krolzig HM. 1997. Markov-Switching Vector Autoregression: Modelling, Statistical Inference and Application to Business Cycle Analysis. Springer: Berlin.
30. Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y. 1992. Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? Journal of Econometrics 54: 159-178.
31. Lucas R. 1976. Econometric policy evaluation: a critique. Carnegie-Rochester Conference on Public Policy 1: 19-46.
32. Ng S, Perron P. 2001. Lag length selection and the construction of unit root tests with good size and power. Econometrica 69: 1519-1554.
33. Swanson N, White H. 1995. A model selection approach to assessing the information in the term structure using linear models and artificial neural networks. Journal of Business and Economics Statistics 13: 265-275.
34. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Blackwell: Oxford.