Capesize markets behavior: Explaining volatility and expectations

Pelagidis, Theodore and Karaoulanis, Ioannis (2021): Capesize markets behavior: Explaining volatility and expectations. Published in: Asian Journal of Shipping and Logistics , Vol. 37, No. 1 (2021)

Abstract

It is widely accepted that the highly volatile capesize market has many peculiarities. Its importance has been recently highlighted by an increase in contribution of the Baltic Capesize Index (BCI) to the Baltic Dry Index (BDI), affecting the progress of the BDI more than any other dry bulk index. This paper investigates the behavior of the capesize market focusing on expectations and time lags. Expectations play a critical role in the freight market both for the short-term and the long-term decision making. In particular, we investigate the relation between time lags and time-charter, trip and spot market rates as well as the average earnings of the capesize vessels of various ages. Time series analysis is used to reach our conclusions. The Hannan – Quinn criterion has been selected to identify the important lags of the capesize freight marketfor the period 1977–2018 and anAutoregressive (AR) model has been constructed to perform the statistical analysis. The findings indicate that there is a strong correlation between time lags and capesize freight market, forecasting indeed the behavior ofthe market. At a practical level, better understanding of the behavior of the capesize market can improve the planning decision of ship-owners and charterers alike.

• Item Type: MPRA Paper
• Language: English
• Keywords: Capesize markets, Freight market, Expectations, Time lag, Volatility
• Subjects: D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D84 - Expectations ; Speculations; F - International Economics > F1 - Trade; G - Financial Economics > G1 - General Financial Markets; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q0 - General > Q02 - Commodity Markets

References

Alexandridis, G., Sahoo, S., & Visvikis, I. (2017). Economic information transmissions and liquidity between shipping markets: New evidence from freight derivatives. Transportation Research Part E, 98, 82–104.

Alizadeh, H. A. (2013). Trading volume and volatility in the shipping forward freight market. Transportation Research Part E, 49, 250–265.

Batchelor, R., Alizadeh, A., & Visvikis, I. (2007). Forecasting spot and forward prices in the international freight market. International Journal of Forecasting, 23, 101–114.

Duru, O., & Yoshida, S. (2009). Judgmental forecasting in the dry bulk shipping business: statistical vs. judgmental approach. The Asian Journal of Shipping and Logistics, 25(2), 189–217.

Goulielmos, M. A., & Psifia, M. E. (2009). Forecasting weekly freight rates for one year time charter 65 000 dwt bulk carrier, 1989-2008, using nonlinear methods. Maritime Policy & Management, 36(5), 411–436.

Greenwood, R., & Hanson, S. (2013). Waves in ship prices and investment Working paper 19246, National Bureau of Economic Research.

Kavussanos, G. M., & Visvikis, D. I. (2004). Market interactions in returns and volatilities between spot and forward shipping freight markets. Journal of Banking & Finance, 28, 2015–2049.

Kavussanos, G. M., Visvikis, D. I., & Batchelor, A. R. (2004). Over-the-counter forward contracts and spot price volatility in shipping. Transportation Research Part E, 40, 273–296.

Lun, Y. H. V., & Quaddus, A. M. (2009). An empirical model of the bulk shipping market. Int. J. Shipping and Transport Logistics, 1(1), 37–54.

Mcconville, J. (1999). Economics of maritime transport. Theory and practice. London, UK: Witherby & Co Ltd.

Pelagidis, T., & Panagiotopoulos, G. (2019). Forward freight agreements and market transparency in the capesize sector. Asian Journal of Shipping and Logistics, 35(3), 154–162.

Qianqian, H., Bo, Y., Guobao, N., & Yu, B. (2014). pp. 1–12. Forecasting dry bulk freight index with improved SVM (2014) Hindawi Publishing Corporation Mathematical Problems in Engineering.

Shuangrui, F., Tingyun, J., Wilmsmeier, G., & Bergqvist, R. (2013). Forecasting baltic dirty tanker indexby applying waveletneuralnetworks. Journal of Transportation Technologies, 3, 68–87.

Stopford, M. (2009). Maritime economics. London, UK: Routledge.

Tsioumas, V., Papadimitriou, S., Smirlis, Y., & Zahran, S. Z.(2017). A novel approach to forecasting the bulk freight market. Asian Journal of Shipping and Logistics, 33(1), 33–41.