  Orthogonal Generalized Autoregressive Conditional Heteroskedasticity model
(OGARCH) is widely used in finance industry to produce volatility and
correlation forecasts. We show that the classic OGARCH model, nevertheless,
tends to be too slow in reflecting sudden changes in market condition due to
excessive persistence of the integral univariate GARCH processes. To obtain
more flexibility to accommodate abrupt market changes, e.g. financial crisis,
we extend classic OGARCH model by incorporating a two-state Markov
regime-switching GARCH process. This novel construction allows us to capture
recurrent systemic regime shifts. Empirical results show that this
generalization resolves the problem of excessive persistency effectively and
greatly enhances OGARCH's ability to adapt to sudden market breaks while
preserving OGARCH's most attractive features such as dimension reduction and
multi-step ahead forecasting. By constructing a global minimum variance
portfolio (GMVP), we are able to demonstrate significant outperformance of the
extended model over the classic OGARCH model and the commonly used
Exponentially Weighted Moving Average (EWMA) model. In addition, we show that
the extended model is superior to OGARCH and EWMA in terms of predictive
accuracy.
