Mathematics – Statistics Theory
Scientific paper
2008-12-04
Mathematics
Statistics Theory
24 pages
Scientific paper
In this paper we study the problem of testing the null hypothesis that errors from k independent parametrically specified generalized autoregressive conditional heteroskedasticity (GARCH) models have the same distribution versus a general alternative. First we establish the asymptotic validity of a class of linear test statistics derived from the k residual-based empirical distribution functions. A distinctive feature is that the asymptotic distribution of the test statistics involves terms depending on the distributions of errors and the parameters of the models, and weight functions providing the flexibility to choose scores for investigating power performance. A Monte Carlo study assesses the asymptotic performance in terms of empirical size and power of the three-sample test based on the Wilcoxon and Van der Waerden score generating functions in finite samples. The results demonstrate that the two proposed tests have overall reasonable size and their power is particularly high when the assumption of Gaussian errors is violated. As an illustrative example, the tests are applied to daily individual stock returns of the New York Stock Exchange data.
No associations
LandOfFree
Testing the equality of error distributions from k independent GARCH models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Testing the equality of error distributions from k independent GARCH models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Testing the equality of error distributions from k independent GARCH models will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-135468