Multivariate modeling and analysis of regional ocean freight rates

Ådland, Roar; Benth, Fred Espen; Koekebakker, Steen

doi:10.1016/j.tre.2017.10.014

Cited by 23 publications

(13 citation statements)

References 53 publications

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“…Tsioumas et al [12] developed a multivariate vector autoregressive model with exogenous variables (VARX) to improve the forecasting accuracy of the BDI, and they found that the VARX model outperforms the ARIMA. Adland et al [13] presented cointegrated time series models in continuous and discrete time to analyze the dynamics of regional ocean freight rates.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Econometric Models and Artificial Neural Networks Algorithms for the Prediction of Baltic Dry Index

2019

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The shipping market, a major component of the global economy, is characterized by high risk and volatility. The Baltic dry index is an influential indicator in the world shipping market and international trade. Several studies have used a variety of techniques to generate Baltic dry index predictions. The most prominent techniques utilize either econometric or artificial intelligence computing. We compare the forecasting accuracy of two typical univariant econometric models and three artificial neural networks (ANNs)-based algorithms. We find that when using daily data, econometric forecasting models produce better one-step-ahead predictions than ANN-based algorithms. When forecasting weekly and monthly data, ANN-based algorithms produce fewer errors and a higher direction matching rate than econometric models. We also compare the predictive power of a number of different models when applied to the 2008 financial crisis and find that the generalized autoregressive conditional heteroskedasticity model and the back propagation neural network algorithm produce the best one-step-ahead and seven-steps ahead predictions, respectively. INDEX TERMS Baltic dry index prediction, ARIMA,GARCH, artificial neural networks(ANN), BP neural network, RBFNN, ELM.

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Section: Introductionmentioning

confidence: 99%

Comparison of Econometric Models and Artificial Neural Networks Algorithms for the Prediction of Baltic Dry Index

2019

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“…In-turn this helps to decrease inventory carrying cost for both, the ultimate customer as well as the logistics operator. It also helps to utilize all the resources optimally with decreased transit time [41]. MMLP contributes in equitable growth of all modes of transportation, proper utilization of pooled assets like railways.…”

Section: B Multimodal Logistics Parks (Mmlp)mentioning

confidence: 99%

An Algorithmic Approach for Maritime Transportation

Pinakpani¹,

Polisetty²,

Bhaskar³

et al. 2020

IJACSA

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Starting from the 3rd millennium BC, Indian maritime trade has augmented the life of a common man and businesses alike. This study, finds that India can leverage on the 7,500 long coast line and derive holistic development in terms of interconnected ports with hinterland connectivity and realize lower expenditure coupled with reduced carbon emission. This research analyzed a decade of cargo data from origination to destination and found that around 82.95 per cent (953 MMTPA in 2017-18) of road based consignments in India comprised of Fertilizers, Hydrocarbons, Coal, Lubricants and Oil. Essentially, a quantum of this i.e. 78.39 per cent of MMTPA cargo consignments (State Owned Hydrocarbons) traverses on Indian roads. The study drew parameters of this transportation paradigm and modeled the same using Artificial Intelligence to depict a monumental opportunity to rationalize costs, improve efficiency and reduce carbon emission to strengthen the argument for the employment of Multimodal Logistics in the Maritime Sector. Subsequent to model derivation the same set of parameters are plotted as an efficient transit map of Interstate transit lines connecting 16 major hubs which now handle bulk cargo shipped by all modes of transport. For the pollution segment a collaborative game theoretic approach i.e., Shapley value is proposed for improved decision making. This study presents data driven and compelling research evidence to portray the benefits of collaboration between firms in terms of time and cost. The study also proposes the need and method to improve hinterland connectivity using a scalable greedy algorithm which is tested with real time data of Coal and Bulk Cargo. As a scientific value addition, this study presents a mathematical model that can be implemented across geographies seamlessly using Information Communication Technology.

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“…In Benth et al [14] and García et al [15], a CARMA process models the spot price in the electricity market, while in Benth et al [6] and Benth and Benth [7] it models weather variables, such as temperature and wind speed. Finally, Andresen et al [16] apply CARMA processes to interest rates modeling and [17] to the dynamics of regional ocean freight rates.…”

Section: Introductionmentioning

confidence: 99%

CARMA Approximations and Estimation

Lavagnini

2020

Front. Appl. Math. Stat.

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Multivariate modeling and analysis of regional ocean freight rates

Cited by 23 publications

References 53 publications

Comparison of Econometric Models and Artificial Neural Networks Algorithms for the Prediction of Baltic Dry Index

Comparison of Econometric Models and Artificial Neural Networks Algorithms for the Prediction of Baltic Dry Index

An Algorithmic Approach for Maritime Transportation

CARMA Approximations and Estimation

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