QUANTILOGRAMS UNDER STRONG DEPENDENCE
研究了长记忆时间序列下分位数图和交叉分位数图的极限理论，提出了移动块自助法进行推断，并应用于金融收益与长记忆预测变量的分位数预测关系。
We develop the limit theory of the quantilogram and cross-quantilogram under long memory. We establish the sub-root-n central limit theorems for quantilograms that depend on nuisance parameters. We propose a moving block bootstrap (MBB) procedure for inference and establish its consistency, thereby enabling a consistent confidence interval construction for the quantilograms. The newly developed reduction principles for the quantilograms serve as the main technical devices used to derive the asymptotics and establish the validity of MBB. We report some simulation evidence that our methods work satisfactorily. We apply our method to quantile predictive relations between financial returns and long-memory predictors.