China commodity price index (CCPI) forecasting via the neural network

Bingzi Jin () and Xiaojie Xu
Additional contact information
Bingzi Jin: Advanced Micro Devices (China) Co., Ltd., Shanghai, P. R. China
Xiaojie Xu: ��North Carolina State University, Raleigh, NC 27695, USA

International Journal of Financial Engineering (IJFE), 2025, vol. 12, issue 03, 1-27

Abstract: Forecasting commodity prices is a vital issue to a wide spectrum of market participants and policy makers in various economic sectors. In this work, we investigate the forecast problem by focusing on the China commodity price index (CCPI). We examine the weekly price index series spanning a 15-year period of June 2, 2006–February 26, 2021 through the nonlinear auto-regressive neural network model. We explore forecast performance corresponding to a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data. We arrive at a model that is relatively simple and generates forecasts of high accuracy and stabilities. Particularly, we reach relative root mean square errors (RRMSEs) of 1.33%, 1.32%, and 1.32% for model training, validation, and testing, respectively, and an overall RRMSE of 1.33% for the whole sample. Our results could, on the one hand, serve as standalone technical price forecasts. They could, on the other hand, be combined with other (fundamental) forecast results for forming perspectives of price trends and carrying out policy analysis.

Keywords: China commodity price index price forecasting; weekly price series data; nonlinear auto-regressive neural network technique (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S2424786325500033
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijfexx:v:12:y:2025:i:03:n:s2424786325500033

Ordering information: This journal article can be ordered from

DOI: 10.1142/S2424786325500033

Access Statistics for this article

International Journal of Financial Engineering (IJFE) is currently edited by George Yuan

More articles in International Journal of Financial Engineering (IJFE) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().