Measuring volatility spillover effects in dry bulk shipping market

Yang, Jialin; Ge, Ying-En; Li, Kevin X.

doi:10.1016/j.tranpol.2022.01.018

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…In particular, most agricultural products, such as cereals, seeds, fodder, sugar, cocoa, and coffee, belong to this category, but there are also products from the construction industry or the mining industry (metal ores, cement, and coal). Such products account for about 38% of the total maritime trade [14,50].…”

Section: Methodsmentioning

confidence: 99%

Selected Polynomial Identification Techniques to Evaluate Maritime Transport Trends Around Covid-19

Borucka¹,

KOZŁOWSKI²

2023

SJSUT.ST

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The Covid-19 pandemic has drastically affected the transport sector, because of the restrictions introduced to limit the spread of the threat. They concerned primarily passenger traffic, but trade in goods also faced completely new challenges, related to increased consumption and the dynamic development of e-commerce on the one hand and restrictions related to the pandemic and sealing borders on the other. One of the most susceptible to fluctuations in international trade is the maritime economy, which has been analysed in this article. It was checked how the global threat affected sea traffic in terms of gross weight of goods handled in main ports. The aim of the study was to characterize the impact of the pandemic on sea transport depending on the type of ship and to evaluate the current state of sea transport in the context of the level shaped by forecasts based on observations from before the coronavirus pandemic. The authors' assumption was to check whether the rail transport market has already reached the level it could reach in the absence of the virus threat. The use of a polynomial function was proposed for the study. Time series containing observations up to the outbreak of the pandemic and forecasts based on them, as well as time series containing additional observations from the pandemic period were analysed. The study results obtained allowed to conclude how the global crisis caused by the Covid-19 pandemic affected the cargo traffic in the sea transport, expressed by the mass of goods transshipped in major ports, depending on the individual types of ships.

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Section: Methodsmentioning

confidence: 99%

Selected Polynomial Identification Techniques to Evaluate Maritime Transport Trends Around Covid-19

Borucka¹,

KOZŁOWSKI²

2023

SJSUT.ST

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“…For example, they offer useful insights into setting future sales prices to agricultural commodity processors, provide necessary information for reaching contractual requirements to trading partners, shed light on potential opportunities for seeking profits in spot and futures markets and suggest possible gaps in risk management and policy assessments to policy makers. As price volatilities tend to be rather irregular (Marfatia, Ji, & Luo, 2022; Xu, 2017, Xu, 2020; Yang, Du, Lu, & Tejeda, 2022, Yang, Ge, & Li, 2022), different price levels have immense impacts on business and policy decisions (Ricome & Reynaud, 2022; Wang et al ., 2022; Warren-Vega, Aguilar-Hernández, Zárate-Guzmán, Campos-Rodríguez, & Romero-Cano, 2022; Xu, 2014; Xu & Thurman, 2015), and ultimately on allocations of resources and social welfare (Liu, Fang, Zhang, Zhong, & Chen, 2022; Ma, Zhang, Song, & Yu, 2022; Xu, 2019, Xu, 2019); price forecasting’s significance to the agricultural economic sector should not call for too much motivation.…”

Section: Introductionmentioning

confidence: 99%

Yellow corn wholesale price forecasts via the neural network

Zhang

2023

ECON

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PurposeForecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.Design/methodology/approachThe authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.FindingsThe final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.Originality/valueThrough the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

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“…Chinese financial markets are rapidly developing and becoming influential in global financial markets ( Fang et al., 2021 ; Cao and Xie, 2022 ). The shipping market played an irreplaceable role in global supply chains and international trade, thereby affecting financial markets ( Gong et al., 2020 ; Yang et al., 2022 ). The Chinese economy was affected by the COVID-19 pandemic ( Kim et al., 2021 ) in multiple ways, including an increase in unemployment and risk aversion, the interruption of supply chains, and a reduction in international trade, thereby generating risk spillover across Chinese financial and shipping markets ( Dai et al., 2022 ; Mensi et al., 2021 ; Si et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%