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

@article{citeulike:2450094, 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 {\copyright} 2006 John Wiley \& Sons, Ltd.}, address = {Department of Accounting and Finance, The Philips College, Cyprus; Department of Business Studies, The Philips College, Cyprus; Department of Economics, University of Crete, Greece}, author = {Constantinou, Eleni and Georgiades, Robert and Kazandjian, Avo and Kouretas, Georgios P.}, citeulike-article-id = {2450094}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/ijfe.305}, citeulike-linkout-1 = {http://www3.interscience.wiley.com/cgi-bin/abstract/113375880/ABSTRACT}, doi = {10.1002/ijfe.305}, journal = {International Journal of Finance \& Economics}, keywords = {ann, predict, stock}, number = {4}, pages = {371--383}, posted-at = {2008-03-01 05:05:46}, priority = {2}, title = {Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns}, url = {http://dx.doi.org/10.1002/ijfe.305}, volume = {11}, year = {2006} }

TY - JOUR ID - citeulike:2450094 L3 - citeulike-article-id:2450094 N2 - 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. IS - 4 JF - International Journal of Finance & Economics AD - Department of Accounting and Finance, The Philips College, Cyprus; Department of Business Studies, The Philips College, Cyprus; Department of Economics, University of Crete, Greece EP - 383 TI - Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns VL - 11 SP - 371 KW - ann KW - predict KW - stock AU - Constantinou, Eleni AU - Georgiades, Robert AU - Kazandjian, Avo AU - Kouretas, Georgios PY - 2006/// UR - http://dx.doi.org/10.1002/ijfe.305 ER -