Financial Time Series Classification by Nonparametric Trend Estimation

被引：0

作者：

Feo, Giuseppe ^{[1
]}

Giordano, Francesco ^{[1
]}

Niglio, Marcella ^{[1
]}

Parrella, Maria Lucia ^{[1
]}

机构：

[1] Univ Salerno, I-84084 Fisciano, SA, Italy

来源：

MATHEMATICAL AND STATISTICAL METHODS FOR ACTUARIAL SCIENCES AND FINANCE, MAF 2022 | 2022年

关键词：

High-dimensionality; Nonparametric regression; Screening procedure; Mixing processes; Financial time series;

D O I：

10.1007/978-3-030-99638-3_39

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

This work considers the classification of financial nonstationary time series, where the nonstationarity is due to the presence of a deterministic trend. It is evaluated in a high-dimensional context by looking at the first derivative of the trend function and without requiring a pre-specified form. This is achieved by means of a nonparametric estimator which is used in a two stage procedure: the first stage selects the time series with no trend and the second stage focuses the attention on nonlinear trends. A real data application to US Mutual Funds is conducted to demonstrate the validity and applicability of the procedure.

引用

页码：241 / 246

页数：6

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