In this issue, readers will find eight articles highlighting the applicability of probability and statistics in finance. Although these articles only cover a small fraction of statistical applications in finance, they provide a glimpse of state of the art research in financial econometrics. Poon et al. consider the dependence of extreme values in international stock markets. The dependence plays a key role in asset allocation and assessing value at risk in the global investment. Kou studies a well-defined, but important problem of pricing barrier options in discrete time points. Fan et al. provide a nice summary and demonstration of non-parametric estimation of stochastic diffusion equation for interest rate dynamics. Local polynomial smoothing proves effective in describing various features of the underlying process. Brown et al. and Wong and So investigate various properties of generalized autoregressive conditional heteroscedastic (GARCH) models. These two papers demonstrate clearly the importance of understanding the second moment in financial application. Andreou and Ghysels study the problem of testing for structural break in conditional co-movements of asset returns. The authors use a two-step procedure to overcome the curse of dimensionality. Zhang et al. apply a generalized information criterion to select portfolios that track the performance of a chosen investment target. Finally, Huerta et al. use hierarchical mixtures of experts to analyze financial time series that exhibit nonlinearity and/or nonstationarity.
Statistics and probability have much to offer in business and economics, especially in finance and marketing. We hope that publication of this theme topic will move more statisticians into financial econometrics. Jointly we can make significant contributions to this important area.
Ruey S. Tsay, Chicago, IL
Guest Editor