Forecasting spot prices in bulk shipping using multivariate and univariate models

Geomelos, Nikolaos D.; Xideas, Evangelos

doi:10.1080/23322039.2014.932701

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…A strong convergence between the forward rates and the spot rates is shown, i.e., the forward rates do help predict the spot rates. Study in [20] focuses on spot price prediction from two aspects: (1) multivariate models (VAR and VECM) and (2) univariate models (ARIMA, GARCH and E-GARCH), so as to obtain the best prediction model for each ship type (tankers and bulk carriers). In addition, the prediction results are modified by combinatorial method theory.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

2023

ITC

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Seaborne crude oil remains the main source of energy in the modern world in terms of volume, accounting for nearly half of all internationally traded crude oil. The shipping market is already characterized by high volatility, coupled with the impact of COVID-19 lockdown and geopolitics events. Price forecasting has become a necessary and challenging task for shipowners and other stakeholders. In the shipping market forecasting literature, the usual focus is on the newbuilding ship price or freight rate. A limited number of literature is for secondhand tanker price. On the other hand, there is few literature that use wavelet neural networks based on adaptive genetic algorithm (AGA-WNN) to predict shipping market. This paper mainly studies the application of the hybrid model to secondhand price prediction of 5 kinds of tanker sizes. The performance of AGA-WNN on time series of 10 and 15 years is compared with the basic performance provided by the six benchmark models, using three error metrics and two statistical tests. We can point out that AGA-WNN provides encouraging and promising results, outperforming the baseline models in both accuracy and robustness. It can be said that AGA-WNN gives the best overall predictive performance.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

2023

ITC

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“…O modelo de regressão dinâmica possui a seguinte equação: (Geomelos e Xideas, 2014;Li e Parsons, 1997;Santos et al, 2014;Tsioumas et al, 2017). O cenário pessimista consiste em uma redução da produção mundial de petróleo de 1% no período de seis meses, redução da importação de petróleo pelos EUA e a manutenção da frota de navios do tipo VLCC.…”

Section: Figura -Previsão Da Variável Causal Frota Vlcc (Mmdwt)unclassified

Forecasting Tanker Freight Rate

Bertoloto

Oliveira

2019

Operations Management for Social Good

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Crude oil and oil products seaborne transportation is a key component of the petroleum industry supply chain, integrating suppliers and customers located in different geographic regions. In this context, the freight rates practiced have a great impact on the international trade of these goods. This work aims to verify the performance of Dynamic Regression models in short-term maritime freight forecasts of the spot market of an oil export route from West Africa to China, to compare the predictive capacity of the model with traditional methods, widely discussed in the literature, such as Exponential Smoothing and ARIMA models and to design scenarios to evaluate how the explanatory variables present in the Dynamic Regression model proposed in this study affect freight rate. The product developed in this dissertation showed the viability of the univariate and causal models being used as a forecasting tool for the oil tankers freight rate. As a form of validation, the results were compared to those obtained with the methodology of a large Brazilian oil company. The proposed prediction system prototype, through Dynamic Regression model, presented satisfactory results and performance superior to that obtained through the methodology of the oil company.

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“…Thus, combinations of the best model of each “class”—the classical time series models and the machine learning approaches—are also estimated, and their forecasting performance of the BDI is compared. Geomelos and Xideas (2014) have followed this approach to forecast freight rates in five subsegments of the tanker sector (ULCC–VLCC, Suezmax, Aframax, Panamax, and Handysize) and three subsegments of dry bulk carriers (Capesize, Panamax, and Handymax), but not for the BDI. In order to improve the forecasting accuracy, they used simple mean combination of vector autoregressive (VAR) or vector error correction model (VECM), ARMA, and GARCH forecasts.…”

Section: Introductionmentioning

confidence: 99%

Time series forecasting methods for the Baltic dry index

Katris

Kavussanos

2021

Journal of Forecasting

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This paper forecasts the daily Baltic Dry Index (BDI) using time series and machine learning methods. Significant business cycles and freight rate volatility present in the ocean‐going shipping industry make the ability to forecast freight rates and cycles extremely important for business decisions. Data‐driven model selection based on data characteristics is performed through ARIMA, fractional ARIMA (FARIMA), and ARIMA and FARIMA models with GARCH and EGARCH errors. The corresponding machine learning techniques utilized are feed‐forward fully connected artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). Among others, FARIMA and MARS models are used for the first time in forecasting the BDI. Diebold–Mariano tests reveal that time series and machine learning approaches have comparable performance. However, combinations of forecasts of the selected models lead to better forecasting accuracy overall. Bai and Perron tests are utilized to check the robustness of the results over different cycles through the detection of breakpoints in the series.

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Forecasting spot prices in bulk shipping using multivariate and univariate models

Cited by 7 publications

References 25 publications

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

Forecasting Tanker Freight Rate

Time series forecasting methods for the Baltic dry index

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