This paper provides an analysis of regime switching in volatility and out-of-sample
forecasting of the Cyprus Stock Exchange 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 a 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 using
two competing non-linear 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 (1995) test and
forecast encompassing, using the Clements and Hendry (1998) test. The results
suggest that both non-linear models equivalent in forecasting accuracy and forecasting
encompassing and therefore on forecasting performance.
01/01/2005
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