Additive fractional models for large random fields applied to high-frequency financial data

应用于高频金融数据的大随机场的加性分数模型

• 批准号: 299304090
• 负责人: Professor Dr. Yuanhua Feng
• 金额: --
• 依托单位: Lehrstuhl für Ökonometrie und quantitative Methoden der empirischen Wirtschaftsforschung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/299304090?language=en
• 关键词: Additive; fractional; models; large; random

We propose to represent nonnegative high-frequency financial data such as squared returns and volatility indexes as random fields on a lattice, where the lattice is defined by the trading days and the trading time points on a day. These data can be analyzed using an additive spatial model by means of the Box-Cox transformation. The goal of this project is to estimate a nonstationary smooth regression surface and a stationary component with short- and long memory as well as antipersistence in both dimensions simultaneously. The regression surface is fitted using a quick double conditional smoothing technique. The bandwidths will be selected with an iterative plug-in algorithm. The stationary components are then estimated using a spatial FARIMA model. The effects of the Box-Cox-Transformation on the resulting estimators will be investigated in detail. Possible extensions and some further problems will also be discussed. The practical relevance of the proposals will be illustrated through application and simulation. The results of this project can also be easily adjusted to analyze similar spatial data from other research areas, such as Physics, Medicine, Biology and Ecology.

我们建议将非负高频金融数据（如平方收益率和波动率指数）表示为格上的随机场，其中格由交易日和交易时间点定义。这些数据可以通过Box-Cox变换使用加性空间模型进行分析。这个项目的目标是估计一个非平稳光滑的回归表面和一个平稳的组成部分，短期和长期的记忆，以及反持久性在两个维度上同时。回归曲面拟合使用快速双条件平滑技术。将使用迭代插件算法选择带宽。然后使用空间FARIMA模型估计固定分量。将详细研究Box-Cox变换对所得估计量的影响。可能的扩展和一些进一步的问题也将被讨论。将通过应用和模拟说明这些建议的实际意义。该项目的结果也可以很容易地进行调整，以分析来自其他研究领域的类似空间数据，如物理学，医学，生物学和生态学。

项目成果

Data-driven local polynomial for the trend and its derivatives in economic time series

• DOI: 10.1080/10485252.2020.1759598
• 发表时间: 2020-04
• 期刊: Journal of Nonparametric Statistics
• 影响因子: 1.2
• 作者: Yuanhua Feng;T. Gries;Marlon Fritz