Measuring the Degree of Distribution Changes Under Local Stationarity
本文在局部平稳框架下提出两种基于特征函数的度量，用于量化分布随时间变化的强度，并结合依赖野自助法估计置信带，通过模拟和股票对数收益应用验证方法。
ABSTRACT The present article introduces new methods for quantifying how intensively the distribution changes over time in a quite general locally stationary framework. Concretely, two characteristic function‐based measures for quantifying the intensity of distribution changes are introduced and estimated. By combining these estimators with suitable dependent wild bootstrap procedures, confidence bands for both measures are estimated. The coverage ratios of the estimated confidence intervals are investigated for finite sample sizes by using simulation studies. As applications, the distribution change intensity of the log returns corresponding to several stocks in chosen time periods is investigated.