Regional Commodities Price Volatility Assessment Using Self-driven Recurrent Networks

Negri, Pablo; Ramos, Priscila Zonzini; Breitkopf, Martin

doi:10.1007/978-3-030-93420-0_34

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Given that commodity prices commonly display patterns of irregular volatility Zhang, 2023p, 2024e;Negri et al, 2021;Xu, 2017aXu, , 2020Bisht, 2019;Shiferaw, 2012;, influence market participants' decisions significantly (Warren-Vega et al, 2022;Xu and Thurman, 2015b;Moe et al, 2008;Xu, 2014c;Wang et al, 2022;, and ultimately effect resource allocations and overall economic prosperity (Jin and Xu, 2024b;Shahid et al, 2022;Xu, 2019a, c;Fan et al, 2022;, it may not be necessary to emphasize the relevance of price estimates for certain commodities.…”

Section: Introductionmentioning

confidence: 99%

Wholesale price forecasts of green grams using the neural network

Jin,

2024

AJEB

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PurposeAgriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.Design/methodology/approachIn order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.FindingsOur model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.Originality/valueUtilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

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Section: Introductionmentioning

confidence: 99%

Wholesale price forecasts of green grams using the neural network

Jin,

2024

AJEB

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“…The importance of the commodity price forecasting problem for scrap steel of the metal sector in China should be of no exception, particularly when one considers its significant role to the general public [16][17][18][19][20][21][22], great influences from volatile macro-economic and policy factors and financial markets on prices [23][24][25][26][27][28], and close connections with many other economic sectors and industries [29][30][31][32][33][34][35][36][37][38][39]. Existing studies have clearly demonstrated that commodity prices tend to reveal patterns of irregular volatilities [40][41][42][43][44][45][46][47][48], they have large impacts on various market participants' decision-making processes [49][50][51][52][53][54], and they ultimately will influence allocations of social resources and thus economic welfare in general [55][56][57][58]. The extreme importance of forecasting their prices might not call for too much motivation.…”

Section: Introductionmentioning

confidence: 99%

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

Zhang

2023

Ironmaking & Steelmaking

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There is little doubt about importance of forecasting commodity prices to policy makers and diverse varieties of market participants. In this present work, we analyse price forecasting problems for scrap steel for east, north, south, central, northeast, and southwest China and at the national level with daily data during 08/23/2013-04/15/2021. We focus on exploration of usefulness of non-linear autoregressive neural networks and examine forecasting performance based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data into training, validation, and testing phases. We arrive at relatively simple models for the seven price series, which lead to forecasts of high accuracy and stabilities with relative root mean square errors below 0.85%. Our results could serve as standalone technical forecasts. They could be combined with other forecasts for forming perspectives of price trends and carrying out policy analysis.

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Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products

Zhang

2022

Miner Econ

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Regional Commodities Price Volatility Assessment Using Self-driven Recurrent Networks

Cited by 3 publications

References 11 publications

Wholesale price forecasts of green grams using the neural network

Wholesale price forecasts of green grams using the neural network

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products

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