Dynamics of Dry Bulk Freight Market: Through the Lens of a Common Stochastic Trend Model

Ko, Byoung-Wook

doi:10.1016/s2092-5212(11)80018-0

“…Moreover, this forecasting applicability "is not only apparent on a short-term basis, but also it is persistent at longer horizons" (Apergis & Payne, 2013). With that said, the "BDI occasionally over-or under-states the status of global dry bulk freight market" dynamics (Ko, 2011).…”

Section: Resultsmentioning

confidence: 99%

“…The BDI includes "the four largest dry-vessel classes" and is calculated as a weighted average (Bakshi et al, 2010). These include indices for the following: Capesize, Handysize, Panamax, and Supramax (Ko, 2011). These four classes include transportation of "iron, coal, cement, grain", metallic ores and other building materials (Oomen, 2012).…”

Section: Baltic Dry Indexmentioning

confidence: 99%

“…With regard to the BDI and its use in other markets, "this volatility is harmful in the sense that their asset is exposed to high risk" (Ko, 2010). As a result, the "BDI occasionally over-or under-states the status of global dry bulk freight market" dynamics (Ko, 2011). In addition, since 2003, there is a general increase in volatility in the BDI, especially surrounding the GFC time frame (Ko, 2010).…”

Section: The Bdi As a Predictormentioning

confidence: 99%

See 1 more Smart Citation

Are Bulk/Container Shipping Companies Improving Compared to the Baltic Dry Index?

Barrows¹

2019

ASS

2

0

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This study evaluates the 2000-2017 time frame and assesses the performance of the bulk/container shipping industry before and after the Great Financial Crisis (GFC) in relation to the Baltic Dry Index (BDI) and two other benchmarks in a variety of combinations. This study evaluates two different period portfolios of shipping companies based on their stock price total return performance. Five cases are presented that demonstrate portfolio improvement when comparing performance after the GFC with performance before the GFC in relation to the BDI and the other benchmarks. Included are discussions on shipping industry competition, vessel utilization and freight rates plus the BDI as an economic activity predictor.

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“…Assume the FFA is settled over the period [T 1 , T 2 ], T 1 < T 2 , and denote the FFA price F (t, T 1 , T 2 ), t ≤ T 1 . From Benth et al [13], a no-arbitrage relationship between F (t, T 1 , T 2 ) and F (t, T ) is (32)…”

Section: 1mentioning

confidence: 99%

Multivariate Modeling and Analysis of Regional Ocean Freight Rates

Ådland

¹

,

Benth

²

,

Koekebakker³

2017

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In this paper, we propose a new multivariate model for the dynamics of regional ocean freight rates. We show that a cointegrated system of regional spot freight rates can be decomposed into a common non-stationary market factor and stationary regional deviations. The resulting integrated CAR process is new to the literature. By interpreting the common market factor as the global arithmetic average of the regional rates, both the market factor and the regional deviations are observable which simplifies the calibration of the model. Moreover, forward contracts on the market factor can be traded in the Forward Freight Agreement (FFA) market. We calibrate the model to historical spot rate processes and illustrate the term structures of volatility and correlation between the regional prices and the market factor. Our model is an important contribution towards improved modelling and hedging of regional price risk when derivative market liquidity is concentrated in a single global benchmark. Date: Monday 18 th September, 2017. Key words and phrases. Freight rates, autoregressive models in continuous time, cointegration, Forward freight agreements. We are grateful to Clarkson Research for provding us with data. The authors acknowledge financial support from the research project "Green shipping under uncertainty" partly financed by the Research Council of Norway under grant number 233985. We thank three referees for their valuable and constructive criticism on an earlier version of the paper.

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“…An alternative approach is the state space representation, where the common stochastic process is unobservable and extracted empirically using, for instance, the Kalman filter approach (Chang et al [19]). Ko [31,32] uses the latter methodology to derive a common stochastic trend from average freight rate indices for different vessel sizes in the drybulk freight market and assess the idiosyncratic dynamics of size effects.…”

Section: Introductionmentioning

confidence: 99%

Multivariate modeling and analysis of regional ocean freight rates

Ådland

¹

,

Benth

²

,

Koekebakker

³

2018

Transportation Research Part E: Logistics and Transportation Re

View full text Add to dashboard Cite

In this paper, we propose a new multivariate model for the dynamics of regional ocean freight rates. We show that a cointegrated system of regional spot freight rates can be decomposed into a common non-stationary market factor and stationary regional deviations. The resulting integrated CAR process is new to the literature. By interpreting the common market factor as the global arithmetic average of the regional rates, both the market factor and the regional deviations are observable which simplifies the calibration of the model. Moreover, forward contracts on the market factor can be traded in the Forward Freight Agreement (FFA) market. We calibrate the model to historical spot rate processes and illustrate the term structures of volatility and correlation between the regional prices and the market factor. Our model is an important contribution towards improved modelling and hedging of regional price risk when derivative market liquidity is concentrated in a single global benchmark.

show abstract

Dynamics of Dry Bulk Freight Market: Through the Lens of a Common Stochastic Trend Model

Cited by 12 publications

References 17 publications

Are Bulk/Container Shipping Companies Improving Compared to the Baltic Dry Index?

Are Bulk/Container Shipping Companies Improving Compared to the Baltic Dry Index?

Multivariate Modeling and Analysis of Regional Ocean Freight Rates

Multivariate modeling and analysis of regional ocean freight rates

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