Authors

• Juan Carlos Escanciano (jescanci@indiana.edu)
  Indiana University
• Silvia Mayoral (smayoral@unav.es)
  Universidad de Navarra

Abstract
A general method for testing the martingale difference hypothesis is proposed. The new tests are data-driven smooth tests based on the principal components of certain marked empirical processes that are asymptotically distribution-free, with critical values that are already tabulated. The data-driven smooth tests are optimal in a semiparametric sense discussed in the paper, and they are robust to conditional heteroskedasticity of unknown form. A simulation study shows that the smooth tests perform very well for a wide range of realistic alternatives and have more power than the omnibus and other competing tests. Finally, an application to the S&P 500 stock index and some of its components highlights the merits of our approach.

Number of Pages:30

Creation Date:2007-01-01

Number:null/07