Nonlinear Dynamics of Daily Cash Prices
发现每日现金价格变化不服从正态分布，存在厚尾和偏斜，且不独立。通过比较扩散跳跃、扩展GARCH和确定性混沌过程，发现残差服从学生分布的GARCH模型最可能，能减少峰度、消除非线性依赖，但无法完全解释非正态性。
Abstract Daily cash price changes are not normally distributed. Their empirical distributions have fat tails and most are skewed. In addition, they are not independent. Among the diffusion‐jump, extended generalized autoregressive conditional heteroskedasticity (GARCH), and deterministic chaos processes, a GARCH process with residuals following a student distribution is the most likely. Our GARCH model reduces leptokurtosis, removes nonlinear dependence, and provides a considerable improvement over the i.i.d. normal model. The GARCH process is not well calibrated because it cannot explain all the observed nonnormality, but it does yield asymptotically valid hypothesis tests.