Angelos Kanas

We find strong evidence of regime switching in the relation between real stock prices and real activity for the UK over the period 1946–2002. Furthermore, there is evidence that the causality pattern from real stock returns to the IP growth rate varies across regimes: causality exists only in the low stock market volatility regime, while it does not exist in the high stock market volatility regime. As the latter largely refers to the period of inflation targeting in the UK, this finding uncovers the importance of monetary policy in insulating the real economy from unexpected financial market effects during periods of financial instability. Additional evidence of regime‐dependence is uncovered using impulse response analysis. Finally, the US stock market is found to exercise significant regime‐independent effects on the UK relation as a whole as well as on the future UK IP growth. Copyright © 2008 John Wiley & Sons, Ltd.

We test for pure contagion effects in international banking arising from the failure of the Bank of Credit and Commerce International (BCCI), one of the largest bank failures in the world. We focused on large individual banks in three developed countries where BCCI had established operations, namely the UK, the US, and Canada. Using event study methodology, we tested for contagion effects using time windows surrounding several known BCCI-related announcements. Our analysis provides strong evidence of pure contagion effects in the UK, which have arisen prior to the official closure date. In contrast, there is no evidence of pure contagion effects in the US and Canada.

Following recent non‐linear extensions of the present‐value model, this paper examines the out‐of‐sample forecast performance of two parametric and two non‐parametric nonlinear models of stock returns. The parametric models include the standard regime switching and the Markov regime switching, whereas the non‐parametric are the nearest‐neighbour and the artificial neural network models. We focused on the US stock market using annual observations spanning the period 1872–1999. Evaluation of forecasts was based on two criteria, namely forecast accuracy and forecast encompassing. In terms of accuracy, the Markov and the artificial neural network models produce at least as accurate forecasts as the other models. In terms of encompassing, the Markov model outperforms all the others. Overall, both criteria suggest that the Markov regime switching model is the most preferable non‐linear empirical extension of the present‐value model for out‐of‐sample stock return forecasting. Copyright © 2003 John Wiley & Sons, Ltd.