Exploiting tail shape biases to discriminate between stable and student t alternatives
针对金融资产收益的厚尾特征，利用稳定分布和学生t分布尾部估计中的相反偏差，提出一种基于符号估计量的方法区分这两种分布，并用实际数据验证了学生t分布更匹配。
Summary The nonnormal stable laws and Student t distributions are used to model the unconditional distribution of financial asset returns, as both models display heavy tails. The relevance of the two models is subject to debate because empirical estimates of the tail shape conditional on either model give conflicting signals. This stems from opposing bias terms. We exploit the biases to discriminate between the two distributions. A sign estimator for the second‐order scale parameter strengthens our results. Tail estimates based on asset return data match the bias induced by finite‐variance unconditional Student t data and the generalized autoregressive conditional heteroscedasticity process.