The dynamic interaction between COVID-19 and shipping freight rates: a quantile on quantile analysis

Khan, Khalid; Su, Chi‐Wei; Khurshid, Adnan; Umar, Muhammad

doi:10.1186/s12544-022-00566-x

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…Additional influential factors, such as shipping reliability and quality of service (Holguín-Veras et al, 2021), should be accounted for in the model if available from more recent data sources. In addition, the current analysis is based on the 2017 data, and the prevalence of freight modes is constantly changing, especially due to the COVID-19 disruptions on road, air, and rail freight transportation (Borca et al, 2021;Khan et al, 2022). The freight mode choice model will be revisited and updated if more recent data and additional attributes become available.…”

Section: Future Research Directionsmentioning

confidence: 99%

Teaching freight mode choice models new tricks using interpretable machine learning methods

Xu,

Yang,

Jeong

et al. 2024

Front. Future Transp.

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Understanding and forecasting complex freight mode choice behavior under various industry, policy, and technology contexts is essential for freight planning and policymaking. Numerous models have been developed to provide insights into freight mode selection; most use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables despite potential nonlinear relationships in the data. A common challenge for researchers is the absence of a heuristic and efficient method to discern and define these complex relationships in logit model specifications. This often results in models that might be deficient in both predictive power and interpretability. To bridge this gap, we develop an MNL model for freight mode choice using the insights from machine learning (ML) models. ML models can better capture the nonlinear nature of many decision-making processes, and recent advances in “explainable AI” have greatly improved their interpretability. We showcase how interpretable ML methods help enhance the performance of MNL models and deepen our understanding of freight mode choice. Specifically, we apply SHapley Additive exPlanations (SHAP) to identify influential features and complex relationships to improve the MNL model’s performance. We evaluate this approach through a case study for Austin, Texas, where SHAP results reveal multiple important nonlinear relationships. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model. Findings from this study can be used to guide freight planning and inform policymakers about how key factors affect freight decision-making.

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Section: Future Research Directionsmentioning

confidence: 99%

Teaching freight mode choice models new tricks using interpretable machine learning methods

Xu,

Yang,

Jeong

et al. 2024

Front. Future Transp.

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show abstract

“…The array of studies on tanker freight rate volatility represents a vital component of maritime economics, with ramifications extending into the broader global economy [30][31][32][33][34]. These time series analyses delve into the interplay between numerous variables affecting freight rates, such as crude oil prices, charter rates, fleet size, and policy changes, aiming to enhance forecasting models and risk management practices within the shipping industry [35][36][37][38][39][40]. Multifractal analysis has emerged as a new trend in these studies，gaining traction due to its ability to capture the asymmetric nature of market risks, demonstrating different magnitudes of response to upward and downward trends and uncovers various scaling behaviors within the data [41][42][43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Multifractal Dynamics and Complexity Transformation in the Tanker Freight Market

Chen,

Liang

2024

Preprint

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This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

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“…Sea freight rates increased around ten times higher from pre-pandemic lows (from 2019 to early 2020) to the worst time in 2021, its autumn (e.g., Freightos FBX index in containers, and Baltic Dry Index in bulk shipping; e.g., Rao and Saul 2021;Investing.com 2022). Research has confirmed that these extreme price changes were tied to the pandemic time and its numerous restrictions and lockdowns (Rožić et al 2022;Khan et al 2022). Purchasing of components also became increasingly more costly, and availability concerns were high.…”

Section: Introductionmentioning

confidence: 99%

In the Midst of the Coronavirus and Geopolitical Crises—Inventory Efficiency and Challenges Faced in Finland

Hilmola

2022

JRFM

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Before the COVID-19 pandemic, the world lived through loose monetary policy and low interest rates. These were further reinforced in 2020, and product-based demand increased throughout the world. Due to these, as well as the suddenly developing geopolitical crisis in Ukraine, inflation started to accelerate (both consumer and producer), and this was especially the case in Europe. Therefore, there is a need for descriptive analysis on how trade and manufacturing companies have reacted to the existing multifaced crisis. This research used data of Finnish publicly traded companies. On the basis of the results, inventories increased in the longer term, especially in 2021 and 2022 (the first half of the year). Content analysis revealed reasons for inventory build-up in 2021–2022, with these being the result of many different causes. In some cases, business expansion or decline was said to be the reason, while in others, it was availability issues faced and purchasing price increases experienced. Interestingly, Russia was directly mentioned as a reason by only a few companies.

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The dynamic interaction between COVID-19 and shipping freight rates: a quantile on quantile analysis

Cited by 9 publications

References 45 publications

Teaching freight mode choice models new tricks using interpretable machine learning methods

Teaching freight mode choice models new tricks using interpretable machine learning methods

Multifractal Dynamics and Complexity Transformation in the Tanker Freight Market

In the Midst of the Coronavirus and Geopolitical Crises—Inventory Efficiency and Challenges Faced in Finland

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