ISSN: 2222-6990
Open access
With the increased globalization the stock markets are integrated more than ever. Increased correlations among assets at global level have severe implications for the economies and industries specifically after the 2008 financial crisis. Following the crisis, another surge in oil price coupled with lower global demand has severely hit marine shipping industry. Therefore, we investigate the return spillovers from oil to the biggest tanker shipping companies of the world i.e. Frontline and Stolt Nielsen listed at Oslo Stock Exchange. We employed VAR DCC-GARCH and found a higher correlation among tanker companies than with the oil. Not surprisingly, the return spillovers from oil increased manifold soon after the financial crisis. The same increased level of correlation was observed for the tanker firms also following crisis period.
Abadie, L. M., Goicoechea, N., & Galarraga, I. (2017). Adapting the shipping sector to stricter emissions regulations: Fuel switching or installing a scrubber?? Transportation Research Part D, 57, 237–250
Aggarwal, R., Akhigbe, A., & Mohanty, S. K. (2012). Oil price shocks and transportation firm asset prices. Energy Economics, 34(5), 1370–1379
Arouri, M. E. H., Jouini, J., & Nguyen, D. K. (2012). On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness. Energy Economics, 34(2), 611–617
Bialystocki, N., & Konovessis, D. (2016). On the estimation of ship’s fuel consumption and speed curve: A statistical approach. Journal of Ocean Engineering and Science, 1(2), 157–166.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327
Chou, M.-T., Chou, T.-Y., Hsu, Y.-R., & Lu, C.-P. (2017). Fuel Consumption Ratio Analysis for Transiting from Various Ports and Harbours in Asia through the Northern Sea Route. Journal of Navigation, 70(04), 859–869
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association.
Diebold, F. X. K. Y. (2008). MEASURING FINANCIAL ASSET RETURN AND VOLATILITY SPILLOVERS, WITH APPLICATION TO GLOBAL EQUITY MARKETS Francis, 119(08), 158–171.
Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(534), 158–171.
Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66.
Drobetz, W., Schilling, D., & Tegtmeier, L. (2010). Common risk factors in the returns of shipping stocks. Maritime Policy and Management, 37(2), 93–120.
Du, X., Yu, C. L., & Hayes, D. J. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33(3), 497–503.
Elveness, V. B., & Widiantoro, D. M. (2011). Managing Risk in Financial Market in Shipping Industry. SSRN Electronic Journal
Engle, R. (2002). DYNAMIC CONDITIONAL CORRELATION – A SIMPLE CLASS OF MULTIVARIATE GARCH MODELS July 1999 Revised Jan 2002 Forthcoming Journal of Business and Economic Statistics 2002. Journal of Business, (July 1999), 1–34.
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987
Fasanya, I., & Akinbowale, S. (2019). Modelling the return and volatility spillovers of crude oil and food prices in Nigeria. Energy, 169, 186–205
Hamdi, B., Aloui, M., Alqahtani, F., & Tiwari, A. (2018). Relationship between the oil price volatility and sectoral stock markets in oil-exporting economies: Evidence from wavelet nonlinear denoised based quantile and Granger-causality analysis. Energy Economics,
Hamilton, J. D. (1983). Oil and the Macroeconomy since World War II. Journal of Political Economy, 91(2), 228–248
Huang, R. D., Masulis, R. W., & Stoll, H. R. (1996). Energy shocks and financial markets. Journal of Futures Markets, 16(1), 1–27.
Ji, Q., & Fan, Y. (2012). How does oil price volatility affect non-energy commodity markets? Applied Energy, 89(1), 273–280
Kang, S. H., McIver, R., & Yoon, S.-M. (2017). Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics, 62, 19–32.
Luo, J., & Ji, Q. (2018). High-frequency volatility connectedness between the US crude oil market and China’s agricultural commodity markets. Energy Economics, 76, 424–438.
Maghyereh, A. I., Awartani, B., & Abdoh, H. (2019). The co-movement between oil and clean energy stocks: A wavelet-based analysis of horizon associations. Energy, 169(2019), 895–913
Mork, K. A. (1989). Oil and the Macroeconomy Whe
In-Text Citation: (Riaz, Hongbing, Sultanuzzaman, & Hashmi, 2019)
To Cite this Article: Riaz, A., Hongbing, O., Sultanuzzaman, M. R., & Hashmi, S. H. (2019). Correlations and Return Spillovers between Oil and the Oslo Tanker Firms. International Journal of Academic Research in Business and Social Sciences, 9(1), 526–536.
Copyright: © 2018 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode