Evaluating Forecast Distributions in Neural Network HAR-Type Models for Range-Based Volatility

被引：0

作者：

La Rocca, Michele ^{[1
]}

Perna, Cira ^{[1
]}

机构：

[1] Univ Salerno, Dept Econ & Stat, Via Giovanni Paolo II 132, I-84084 Fisciano, SA, Italy

来源：

ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2024 | 2024年 / 2141卷

关键词：

Range-based volatility measures; HAR-type models; Feed-forward neural networks; Residual bootstrap; TIME-SERIES; ESTIMATORS; VARIANCE;

D O I：

10.1007/978-3-031-62495-7_38

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we focus on a range-based measure for volatility and present a forecasting tool combining the heterogeneous autoregressive model with feed-forward neural networks. Using a bootstrap scheme, we can also obtain the forecast distributions, which are useful to evaluate how much uncertainty is associated with each point forecast. An application to real data shows a significant contribution of the proposed methodology to improving forecast accuracy in terms of point forecasts and forecast distributions.

引用

页码：504 / 517

页数：14

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