Non-linear mixture models for cross-sectional financial log returns

摘要

High-frequency financial log returns are known to exhibit sharp departures from the usual normality assumption. This article carries out an in-depth evaluation of the effectiveness of multidimensional non-linear mixed models that arise from considering a scale mixture-based approach to modeling non-normal log returns over a cross-sectional period of time. Simulation-based first-order and second-order comparison measures that compare the non-linear mixed model with the linear model show strong preference in favour of the non-linear mixed models. The methodology is illustrated through a thorough analysis of quarterly intra-day log returns from CISCO, DELL, COKE, and S&P500 for the years 1998-2000.