The Effect of Freight Rates on Fleet Productivity: An Empirical Research on Dry Bulk Market

Yıl 2022, Cilt: 3 Sayı: 1, 25 - 34, 31.01.2022

Abdullah Açık Burhan Kayıran

https://doi.org/10.52602/mtl.1051408

Öz

Fleet productivity increases in two directions. First one is achieved by increasing the speed of the vessels in the market conditions where high freight rates are observed, this increases the amount of cargo per unit capacity they carry at the unit time. The other one is related to the short run inelastic supply curve in shipping because of the time to build effect. When the demand increases occur, the amount of cargo carried per unit capacity increases since the increase in supply is limited in the short run. In this context, it is determined the relationship between freight rates and the amount of cargo carried per unit capacity in this study. The Baltic Dry Index (BDI) was selected as a measure of the freight rates, and the tonnage carried per DWT from the portion of the total cargo tonnage carried by the sea to the dry cargo fleet capacity during that year was selected as an indicator of the fleet productivity. The dataset used in the study consists of annual observations covering the period from 1985 to 2020. Correlation and regression methods were used to determine the econometric relationship between the variables. As a result of the study, a significant strong relationship was found between freight rates and productivity in the positive direction. According to the developed model, a 10% increase in the freight rate causes an increase of about 1.3% in fleet productivity.

Anahtar Kelimeler

Dry Bulk Market, Freight Rates, Fleet, Productivity

Kaynakça

• Angelopoulos, J. (2017). Time–Frequency Analysis of the Baltic Dry Index. Maritime Economics & Logistics, 19(2), 211-233.
• Bakshi, G, Panayotov, G., and Skoulakis, G. (2011). The Baltic Dry Index as a Predictor of Global Stock Returns, Commodity Returns, and Global Economic Activity. American Finance Association Meetings (AFA).
• Bloomberg (2018). Baltic Dry Index. Bloomberg Data Platform.
• Chang, M. (2014). Principles of Scientific Methods. CRC Press.
• Derindere Köseoğlu, S. (2011). Is Baltic Dry Index a Good Leading Indicator for Monitoring the Progress of Global Economy? The 9th. International Logistics and Supply Chain Congress, International Retail Logistics in the Value Era, Prooceedings Vol.II, Yaşar University, Çeşme, İzmir, Turkey.
• Dickey, D.A., and Fuller, W.A. (1979) Distribution of the Estimators for Autoregressive Time Series with A Unit Root. Journal of the American Statistical Association 74, 366a, pp. 427–431.
• Duru, O. (2010). Theory of shipping productivity revisited: industrial revolution, ship technology and shipping freight rates. The 74th Conference of Japan Society of History of Economic Thought, Toyama.
• Glen, D., and Chisty, S. (2010). The Tanker Market: Current Structure and Economic Analysis. In Grammenos, C. (Ed.), The Handbook of Maritime Economics and Business (pp. 355-390). UK: Lloyd’s List.
• Gordon, R. A. (2015). Regression Analysis for The Social Sciences. Routledge.
• Heidbrink, I. (2011). The Business of Shipping: An Historical Perspective. In Talley, W.K. (Ed.), The Blackwell Companion to Maritime Economics (pp. 34-51). USA: Wiley-Blackwell
• Karakitsos, E., and Varnavides, L. (2014). Maritime Economics: A Macroeconomic Approach. Springer.
• Kärrlander E (2010). Base Metals, A Base for Stock Prices (Unpublished Bachelor thesis). Lund University, Scania, Sweden.
• Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., and Shin, Y. (1992). Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root. Journal of Econometrics 54, 159–178.
• Lin, F. and Sim, N. C. (2013). Trade, Income and The Baltic Dry Index. European Economic Review, 59, 1-18.
• Ljung, G., and G. Box. (1979). On a Measure of Lack of Fit in Time Series Models. Biometrika, 66, 265–270.
• Ma, S. (2020). Economics of Maritime Business. New York: Routledge.
• Newey, W., and West, K. (1987). A Simple Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55, 703–708.
• Osborne, J.W. (2008). Best Practices in Quantitative Methods. London: Sage Publications.
• Ruan, Q., Wang, Y., Lu, X. and Qin, J. (2016). Cross-correlations between Baltic Dry index and crude oil prices. Physica A: Statistical Mechanics and its Applications, 453, 278-289.
• Sen, A., and Srivastava, M. (1990). Regression Analysis: Theory, Methods, and Applications. Springer Science & Business Media.
• Sharma, A. K. (2005). Text Book of Correlations and Regression. Discovery Publishing House.
• Soh, K. (2016). Understanding Test and Exam Results Statistically: An Essential Guide for Teachers and School Leaders. Singapore: Springer.
• Stopford, M. (2009). Maritime Economics. New York: Routledge.
• Tamvakis, M. (2012). International Seaborne Trade. In Talley, W.K. (Ed.), The Blackwell Companion to Maritime Economics (pp. 52-86). USA: Wiley-Blackwell
• Tsolakis, S. (2005). Econometric Analysis of Bulk Shipping Markets: Implications for Investment Strategies and Financial Decision-Making (Doctoral Thesis). Erasmus University Rotterdam.
• UNCTAD (2021). Wolrd Dry Bulk Fleet and World Seaborne Trade. Retrieved December 20, 2021, from http://unctadstat.unctad.org/wds/ReportFolders/reportFolders.aspx.
• White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix and A Direct Test for Heteroskedasticity. Econometrica, 48, 817–838.
• Xiong, T., and Hu, Z. (2021). Soybean Futures Price Forecasting Using Dynamic Model Averaging: Do the Predictors Change Over Time? Emerging Markets Finance and Trade, 57, 1198-1214.