股票收益率序列替代模拟中的异常值和日历效应
Accounting for outliers and calendar effects in surrogate simulations of stock return sequences
摘要：
替代数据分析（SDA）是一种统计假设检验框架，用于确定时间序列动力学中的弱混沌。现有的SDA程序不正确的帐户丰富的结构中观察到的股票收益率序列，归因于异方差，季节性效应和异常值的存在。在本文中，我们提出了一个修改的SDA框架，基于鲁棒估计的平均平稳时间序列的位置和尺度参数和概率框架，处理离群值。对纳斯达克综合指数日收益率的演示表明，所提出的方法产生的代理人，忠实地再现了原始系列的结构，同时表现出的线性随机动态。
英文摘要：
Surrogate Data Analysis (SDA) is a statistical hypothesis testing framework for the determination of weak chaos in time series dynamics. Existing SDA procedures do not account properly for the rich structures observed in stock return sequences, attributed to the presence of heteroscedasticity, seasonal effects and outliers. In this paper we suggest a modification of the SDA framework, based on the robust estimation of location and scale parameters of mean-stationary time series and a probabilistic framework which deals with outliers. A demonstration on the NASDAQ Composite index daily returns shows that the proposed approach produces surrogates that faithfully reproduce the structure of the original series while being manifestations of linear-random dynamics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
