The non-linear dynamics of spot freight rates in tanker markets

Ådland, Roar; Cullinane, Kevin

doi:10.1016/j.tre.2004.12.001

Cited by 103 publications

(46 citation statements)

References 26 publications

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“…Marlow and Gardner (1980) also have an early model on the dry bulk shipping sector, and Beenstock and Vergottis (1989a, b) build an econometric model for the world tanker market and the dry bulk market. In contrast to Adland and Cullinane (2006), Koekebakker et al (2006) and Batchelor et al (2007) are interested in the BDI series as a whole rather than analysing the spot or forward rates separately. More recently, Goulas and Skiadopoulos (2012) analyse the efficiency of the IMAREX futures markets and Sim (2013, 2014) investigate the relationship and effect of the BDI with trade as well as the transitory negative income shocks impacted by the BDI in Sub-Saharan countries.…”

Section: Introductionmentioning

confidence: 99%

The Baltic Dry Index: cyclicalities, forecasting and hedging strategies

2016

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The cyclical properties of the Baltic Dry Index (BDI) and their implications for forecasting performance are investigated. We find that changes in the BDI can lead to permanent shocks to trade of major exporting economies. In our forecasting exercise, we show that commodities and trigonometric regression can lead to improved predictions and then use our forecasting results to perform an investment exercise and to show how they can be used for improved risk management in the freight sector.

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Section: Introductionmentioning

confidence: 99%

The Baltic Dry Index: cyclicalities, forecasting and hedging strategies

2016

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“…Presenting a stochastic optimal control problem the property of the equilibrium of tanker shipping freight rates is discussed to be close to that of the standard geometric mean reversion process [15]. Adland & Cullinane [5] utilized a general non-parametric Markov diffusion method to investigate the dynamics of tanker spot freight rates, arguing that non-linear stochastic models can best describe these dynamics. However, due to the high volatility in spot freight rates, the difficulties in detecting slow-speed mean reversion in high frequency data became a constraint for the model [5].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Adland & Cullinane [5] utilized a general non-parametric Markov diffusion method to investigate the dynamics of tanker spot freight rates, arguing that non-linear stochastic models can best describe these dynamics. However, due to the high volatility in spot freight rates, the difficulties in detecting slow-speed mean reversion in high frequency data became a constraint for the model [5]. Batchelor et al [10] examined the performance of popular time series models in forecasting spot and forward rates on major seaborne freight routes, and they found the vector equilibrium correction model (VECM), to deliver the best in-samplefit, however, the predictive ability of the VECM in reality is poor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[1][2][3][4][5][6], the freight rate complexity in connection to forecasting techniques is of particular interest in this paper. An inherent feature of shipping freight rates are fluctuations as a source of market risks for all market participants, including not only tanker shipping companies, but also hedge funds, commodity and even energy producers [7].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

Fan¹,

Ji²,

Wilmsmeier³

et al. 2013

JTTs

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Baltic Exchange Dirty Tanker Index (BDTI) is an important assessment index in world dirty tanker shipping industry. Actors in the industry sector can gain numerous benefits from accurate forecasting of the BDTI. However, limitations exist in traditional stochastic and econometric explanation modeling techniques used in freight rate forecasting. At the same time research in shipping index forecasting e.g. BDTI applying artificial intelligent techniques is scarce. This analyses the possibilities to forecast the BDTI by applying Wavelet Neural Networks (WNN). Firstly, the characteristics of traditional and artificial intelligent forecasting techniques are discussed and rationales for choosing WNN are explained. Secondly, the components and features of BDTI will be explicated. After that, the authors delve the determinants and influencing factors behind fluctuations of the BDTI in order to set inputs for WNN forecasting model. The paper examines non-linearity and non-stationary features of the BDTI and elaborates WNN model building procedures. Finally, the comparison of forecasting performance between WNN and ARIMA time series models show that WNN has better forecasting accuracy than traditionally used modeling techniques.

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“…An abundance of studies have been carried out in an attempt to understand the time-varying characteristics of freight rate volatility (Kavussanos, 1996a and1996b;Kavussanos, 2003;Lu, Marlow and Wang, 2008; among others), yet among them only a few have discussed what are the causes and impacts of the time-varying risk in shipping markets. For example, Adland and Cullinane (2006) modeled volatility as a function of the level of freight rate themselves. and Batchelor, Alizadeh and Visvikis (2005) studied shipping risk by analyzing the impact of the volatility of shipping derivatives.…”

Section: Introductionmentioning

confidence: 99%

The dynamics between freight volatility and fleet size growth in dry bulk shipping markets

Yip

Marlow

2011

Transportation Research Part E: Logistics and Transportation Re

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This paper studies the relationship between the time-varying volatility of dry bulk freight rates and the change of the supply of fleet trading in dry bulk markets. An abundance of research has been done to understand the time-varying characteristics of freight rate volatility, yet few have discussed the determinants of freight volatility. We therefore examine freight volatility against the changes in fleet size and other shipping market variables over January 1973 to October 2010. The study employs a two-step model specification. The first step is the measurement of freight rate volatility through an AR-GARCH model; the second step is the analysis of the relationship between freight rate volatility and fleet size growth through a GMM regression. We confirm similar findings in the literature that freight rate volatility is time varying. Furthermore, the results reveal that the change in fleet size positively affects freight rate volatility, while the spot rate volatility of Capesize dry bulk exhibits a stronger reaction to the change in fleet size. The 2 results of this study contribute in a general sense to understanding the systematic risk of shipping markets.Key words: Freight markets; Freight volatility; Fleet size; GARCH\ Highlights:We examine the determinants of freight volatility in dry bulk shipping markets.We adopt a two-step model specification: a GARCH model and a GMM regression.Fleet size growth has a positive impact on freight volatility.Freight rate volatility is time varying.

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The non-linear dynamics of spot freight rates in tanker markets

Cited by 103 publications

References 26 publications

The Baltic Dry Index: cyclicalities, forecasting and hedging strategies

The Baltic Dry Index: cyclicalities, forecasting and hedging strategies

Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

The dynamics between freight volatility and fleet size growth in dry bulk shipping markets

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