Forecasting of density functions with an application to cross-sectional and intraday returns
Forecasting of density functions with an application to cross-sectional and intraday returns
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DOI：
10.1016/j.ijforecast.2019.05.007
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发表时间：
2019-10-01
影响因子：
7.9
通讯作者：
Shang, Han Lin
中科院分区：
文献类型：
--
作者：
Kokoszka, Piotr;Miao, Hong;Shang, Han Lin
本文研究概率密度函数的预测问题。密度函数是非负的，并且有一个约束积分，因此不构成向量空间。因此，对这种非线性数据实施既定的函数时间序列预测方法是有问题的。两个新的方法开发和比较两个现有的方法。比较是基于截面和日内回报率得出的密度。对于这样的数据，我们的新方法之一被证明是占主导地位的现有方法，而另一个是现有的方法之一。(C)2019年国际预测研究所。Elsevier B.V.出版，保留所有权利。
This paper is concerned with the forecasting of probability density functions. Density functions are nonnegative and have a constrained integral, and thus do not constitute a vector space. The implementation of established functional time series forecasting methods for such nonlinear data is therefore problematic. Two new methods are developed and compared to two existing methods. The comparison is based on the densities derived from cross-sectional and intraday returns. For such data, one of our new approaches is shown to dominate the existing methods, while the other is comparable to one of the existing approaches. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.