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Journals / Business and Economics Research Journal / 2018 / Cilt: 9 - Sayı: 4

Oil Production as a Leading Indicator for Tanker Freight Market

ABDULLAH AÇIK
SADIK ÖZLEN BAŞER
Pages
773–785
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is hoped that these results may be beneficialto developing business strategies and reducing risks for shipowners and charterers inthe tanker market.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to determine the causality between oil production andfreight rates in the seaborne oil transportation in the world.
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Key Findings & Data:While the Baltic Dirty Tanker Index (BDTI) andBaltic Clean Tanker Index (BCTI) variables are used as the measurement for the tankerfreight rates, the Bloomberg oil production index variable is used as the productionmeasurement.
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Conclusion & Impact:It is hoped that these results may be beneficialto developing business strategies and reducing risks for shipowners and charterers inthe tanker market.
🔬Design:Research Article
📍Scope:2018 Verileri
⚙️Variables:Domain Parameters

Özet

The aim of this study is to determine the causality between oil production andfreight rates in the seaborne oil transportation in the world. The causality with oil priceshas been studied more in the literature, but the studies that examine the causality withoil production have been limited. However, the effect of the crude oil production amounton the market is also great. The methods that can detect delayed causality have beenutilized unlike the existing literature. While the Baltic Dirty Tanker Index (BDTI) andBaltic Clean Tanker Index (BCTI) variables are used as the measurement for the tankerfreight rates, the Bloomberg oil production index variable is used as the productionmeasurement. The data set used in the study consists of 239 monthly observationscovering between August 1998 and June 2018. Standard causality analysis developedby Granger (1969) is used for linear causality, and nonparametric tests developed byHiemstra and Jones (1996) and Diks and Panchenko (2006) are used for nonlinearcausality analysis. While linear causality cannot be spotted in the obtained results,nonlinear causality is determined. As a result of the four lagged nonlinear causalityanalyzes made, the causality is found from the second to the fourth lag for BDTI andfrom the first to the fourth lag for BCTI. According to these results, while the effect ofchanges in production is directly reflected in the product tanker market, it is reflectedon the dirty tanker market with a delay. It is hoped that these results may be beneficialto developing business strategies and reducing risks for shipowners and charterers inthe tanker market.

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Cite This Article

AÇIK, A., BAŞER, S. Ö. (2018). Oil Production as a Leading Indicator for Tanker Freight Market. Business and Economics Research Journal, Cilt: 9 - Sayı: 4, 773–785. https://ajindex.com/articles/352168

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References (13)

  1. Abouarghoub, W. (2011). Measuring shipping tanker freight risk (No. 02/10). Centre for Global Finance Working Paper. Adıgüzel, U., Bayat, T., Kayhan, S., & Nazlıoğlu, Ş. (2013). Oil prices and exchange rates in Brazil, India and Turkey: Time and frequency domain causality analysis. Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi, 1(1), 49-73.
  2. Ajmi, A. N., El Montasser, G., & Nguyen, D. K. (2013). Testing the relationships between energy consumption and income in G7 countries with nonlinear causality tests. Economic Modelling, 35, 126-133.
  3. Alizadeh, A. H., & Nomikos, N. K. (2004). Cost of carry, causality and arbitrage between oil futures and tanker freight markets. Transportation Research Part E: Logistics and Transportation Review, 40(4), 297-316.
  4. Alizadeh, A. H., & Nomikos, N. K. (2011). Dynamics of the term structure and volatility of shipping freight rates. Journal of Transport Economics and Policy (JTEP), 45(1), 105-128.
  5. Alizadeh, A., & Nomikos, N. (2009). Shipping derivatives and risk management. Springer. Angelier, J. P. (1991). The determinants of oil prices. Energy Studies Review, 3(3), 217-226. Archanskaïa, E., Creel, J., & Hubert, P. (2012). The nature of oil shocks and the global economy. Energy Policy, 42, 509-520.
  6. Baek, E., & Brock, W. (1992). A general test for nonlinear Granger causality: Bivariate model. Working Paper. Iowa State University and University of Wisconsin-Madison. Bal, D. P., & Rath, B. N. (2015). Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India. Energy Economics, 51, 149-156.
  7. Balcilar, M., Ozdemir, Z. A., & Cakan, E. (2011). On the nonlinear causality between inflation and inflation uncertainty in the G3 countries. Journal of Applied Economics, 14(2), 269-296.
  8. Bildirici, M. E., & Turkmen, C. (2015). Nonlinear causality between oil and precious metals. Resources Policy, 46, 202-211.
  9. Bloomberg Data Platform (2018).
  10. Oil production index, Baltic dirty tanker index, Baltic clean tanker index. Retrieved August 10, 2018.
  11. Brock, W. (1991). Causality, chaos, explanation and prediction in economics and finance. In: Casti, J., Karlqvist, A. (Eds.), Beyond belief: Randomness, prediction and explanation in science. CRC Press,
  12. Boca Raton, Fla. Brock, W., Davis, D., Jose, S., & Blake, L. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197–235.
  13. Brock, W., Dechect, W., & Scheinkman, J. (1987). A test for independence based on the correlation