This study investigates the size and power properties of three multivariate tests for autocorrelation, namely portmanteau test, Lagrange multiplier (LM) test and Rao F-test, in the stable and unstable vector autoregressive (VAR) models, with and without autoregressive conditional heteroscedasticity (ARCH) using Monte Carlo experiments. Many combinations of parameters are used in the simulations to cover a wide range of situations in order to make the results more representative. The results of conducted simulations show that all three tests perform relatively well in stable VAR models without ARCH. In unstable VAR models the portmanteau test exhibits serious size distortions. LM and Rao tests perform well in unstable VAR models without ARCH. These results are true, irrespective of sample size or order of autocorrelation. Another clear result that the simulations show is that none of the tests have the correct size when ARCH is present irrespective of VAR models being stable or unstable and regardless of the sample size or order of autocorrelation. The portmanteau test appears to have slightly better power properties than the LM test in almost all scenarios.
This article investigates the issue of international portfolio diversification with respect to the three largest financial markets in the world-namely the US, Japan and the UK. In addition to making use of traditional portfolio analysis, we also suggest a procedure to calculate bootstrap correlation coefficients that can take into account the dynamic structure between the markets as measured by bootstrapped causality tests. Weekly data is used. The results from the first approach are supporting international diversification. The bootstrapped causality tests provide additional empirical support for this conclusion since the size of the causal effects is negligible and the bootstrap correlations are similar as the standard ones. (c) 2006 Elsevier B.V. All rights reserved.