Causality Relationship Between Dirty and Clean Tanker Index Volatilities and BRIC Stock Exchanges

Year 2024, Volume: 26 Issue: 3, 915 - 936

İsmail Koçak , Savaş Tarkun , Mehmet Çınar

https://doi.org/10.26745/ahbvuibfd.1470589

Abstract

In this study, the relationship between the Baltic Dirty Tanker Index (BDTI) and the Baltic Clean Tanker Index (BCTI) volatility and the returns of BRIC stock markets was investigated by a frequency domain causality test. In this study, which was investigated with daily data for the period 04.01.2013–29.12.2023, an artificial volatility series was first created for dirty tanker and clean tanker indices. The findings show that there is a bidirectional causality between the Brazilian stock market return and dirty tanker volatility in the long term. A one-way causality relationship was found between the Russian stock market and clean tanker volatility in the medium term, and a statistically significant causality relationship was found between the Indian stock market return and dirty tanker volatility in the long and short term. However, no causal relationship was found between Chinese stock market returns and dirty and clean tanker volatility. These results place Russia among the most important oil-exporting countries in the world. Therefore, it has been demonstrated that the information in the past period returns of this country's stock market, especially of dirty tanker ship owners, contains information on the current period values of dirty tanker freight shocks in the long, short, and medium term. The most important reason for the lack of a causal relationship between Chinese stock market returns and the volatility of crude oil and oil by-product freight rates can be shown as China's emphasis on renewable energy policies.

Keywords

Dirty Tanker Index, Clean Tanker Index, BRIC, Frequency Domain Causality

Kirli ve Temiz Tanker Endeksi Volatiliteleri ile BRIC Borsaları Arasındaki Nedensellik İlişkisi

Abstract

Bu çalışmada, Baltık Kirli Tanker Endeksi (BDTI) ile Baltık Temiz Tanker Endeksi (BCTI) volatilitesinin BRIC borsalarının getirileri arasındaki ilişki frekans alan nedensellik testi ile araştırılmıştır. 04.01.2013-29.12.2023 dönemine ait günlük veriler ile araştırılan bu çalışmada, öncelikle kirli tanker ve temiz tanker endeksleri için yapay volatilite serisi oluşturulmuştur. Ulaşılan bulgular ise uzun vadede Brezilya borsası getirisi ile kirli tanker volatilitesi arasında çift taraflı nedensellik elde edilmiştir. Rusya borsası ile temiz tanker volatilitesi arasında orta vadede tek yönlü, uzun ve kısa vadede Hindistan borsası getirisi ile kirli tanker volatilitesi arasında istatistiksel olarak anlamlı nedensellik ilişkisi bulunmuştur. Ancak Çin borsası getirisi ile kirli ve temiz tanker volatilitesi arasında herhangi bir nedensellik ilişkisi bulunamamıştır. Bu sonuçlar, Rusya, dünyadaki önemli petrol ihracatçısı ülkeler arasında yer almaktadır. Dolayısıyla, özellikle kirli tanker armatörlerinin bu ülke borsasının geçmiş dönem getirilerindeki bilgilerin kirli tanker navlun şoklarının cari dönem değerlerindeki bilgileri barındırdığını uzun, kısa ve orta vadede ortaya konmuştur. Çin borsa getirisi ile ham petrol ve petrol yan ürünü navlun oranları volatilitesi arasında nedensellik ilişkisinin bulunmamasının en önemli nedeni ise Çin’in yenilenebilir enerji politikalarına ağırlık vermesi olarak gösterilebilir.

Keywords

Kirli Tanker Endeksi, Temiz Tanker Endeksi, BRIC, Frekans Alanı Nedensellik

References

• Abubakirova, A., Syzdykova, A., Dosmakhanbet, A., Kudabayeva, L., & Abdulina, G. (2021). Relationship between Oil Prices and Stock Prices in BRICS-T Countries: Symmetric and Asymmetric Causality Analysis. International Journal of Energy Economics and Policy, 11(3), 140–148. https://doi.org/10.32479/ijeep.10487.
• Alam, M. M., & Uddin, M. G. S. (2009). Relationship between Interest Rate and Stock Price: Empirical Evidence from Developed and Developing Countries. International Journal of Business and Management, 4(3), 43–51. https://doi.org/10.5539/ijbm.v4n3p43.
• Alizadeh, A. H., & Muradoglu, G. (2014). Stock market efficiency and international shipping-market information. Journal of International Financial Markets, Institutions and Money, 33, 445–461. https://doi.org/10.1016/j.intfin.2014.10.002.
• Bakshi, G. S., Panayotov, G., & Skoulakis, G. (2011). The Baltic Dry Index as a Predictor of Global Stock Returns, Commodity Returns, and Global Economic Activity. In SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1787757.
• Bekaert, G., & Engstrom, E. (2010). Inflation and the stock market: Understanding the “Fed Model.” Journal of Monetary Economics, 57(3), 278–294. https://doi.org/10.1016/j.jmoneco.2010.02.004.
• Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181–192. https://doi.org/10.1016/j.jeconom.2014.05.008.
• Bhar, R., & Nikolova, B. (2009). Oil prices and equity returns in the BRIC countries. World Economy, 32(7), 1036–1054. https://doi.org/10.1111/j.1467-9701.2009.01194.x.
• Bodart, V., & Candelon, B. (2009). Evidence of Interdependence and Contagion Using a Frequency Domain Framework. Emerging Markets Review, 10(2), 140–150. https://doi.org/10.1016/j.ememar.2008.11.003.
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307–327. https://doi.org/10.1109/TNN.2007.902962.
• Bouri, E., Lien, D., Roubaud, D., & Hussain Shahzad, S. J. (2018). Fear Linkages Between the US and BRICS Stock Markets: A Frequency-Domain Causality. International Journal of the Economics of Business, 25(3), 441–454. https://doi.org/10.1080/13571516.2018.1505241.
• Breitung, J., & Candelon, B. (2006). Testing for Short- and Long-run Causality: A Frequency-Domain Approach. Journal of Econometrics, 132(2), 363–378. https://doi.org/10.1016/j.jeconom.2005.02.004.
• Chi, J. (2016). Exchange rate and transport cost sensitivities of bilateral freight flows between the US and China. Transportation Research Part A: Policy and Practice, 89, 1–13. https://doi.org/10.1016/j.tra.2016.05.004.
• Ekanayake, E. M., & Dissanayake, A. (2022). Effects of Real Exchange Rate Volatility on Trade: Empirical Analysis of the United States Exports to BRICS. Journal of Risk and Financial Management, 15(2). https://doi.org/10.3390/jrfm15020073.
• Erlandsen, U. A., & Gjertsen, F. (2023). Do Shipping Freight Rates Predict Stock Market Return? Norwegian School of Economics.
• Gavriilidis, K., Kambouroudis, D. S., Tsakou, K., & Tsouknidis, D. A. (2018). Volatility forecasting across tanker freight rates: The role of oil price shocks. Transportation Research Part E: Logistics and Transportation Review, 118(September), 376–391. https://doi.org/10.1016/j.tre.2018.08.012.
• Geman, H., & Smith, W. O. (2012). Shipping markets and freight rates: An Analysis of the Baltic Dry Index. Journal of Alternative Investments, 15(1), 98–109. https://doi.org/10.3905/jai.2012.15.1.098.
• Geweke, J. (1982). Measurement of Linear Dependence and Feedback Between Multiple Time Series. Journal of the American Statistical Association, 77(378), 304–313. https://doi.org/10.2307/2287242.
• Hammoudeh, S., Kang, S. H., Mensi, W., & Nguyen, D. K. (2016). Dynamic Global Linkages of the BRICS Stock Markets with the United States and Europe Under External Crisis Shocks: Implications for Portfolio Risk Forecasting. World Economy, 39(11), 1703–1727. https://doi.org/10.1111/twec.12433.
• Helmi, M. H., Nazif Catik, A., Kosedagli, B. Y., Kisla, G. S. H., & Akdeniz, C. (2023). The Effects of Energy Prices on Oil-Gas Sectoral Stock Returns for BRIC Countries: Evidence from Space State Models. International Journal of Energy Economics and Policy, 13(6), 430–440. https://doi.org/10.32479/ijeep.14801.
• Hosoya, Y. (1991). The Decomposition and Measurement of the Interdependency Between Second-Order Stationary Processes. Probability Theory and Related Fields, 88(4), 429–444. https://doi.org/10.1007/BF01192551.
• Iskenderoglu, Ö., & Akdag, S. et. (2020). Comparison of the Effect of Vix Fear Index on Stock Exchange Indices of Developed and Developing Countries: The G20 Case. South East European Journal of Economics and Business, 15(1), 105–121. https://doi.org/10.2478/jeb-2020-0009.
• Jing, L., Marlow, P., & Hui, W. (2008). An analysis of freight rate volatility in dry bulk shipping markets. Maritime Policy and Management, 35(3), 237–251. https://doi.org/10.1080/03088830802079987.
• Khan, K., Su, C. W., Tao, R., & Umar, M. (2021). How Often Do Oil Prices and Tanker Freight Rates Depend on Global Uncertainty? Regional Studies in Marine Science, 48, 102043. https://doi.org/10.1016/j.rsma.2021.102043.
• Kumar, A., Mallick, S., Mohanty, M. S., & Zampolli, F. (2022). Market Volatility, Monetary Policy and The Term Premium (No. 606).
• Lakshmi, P., Visalakshmi, S., & Shanmugam, K. (2015). Intensity of shock transmission amid US-BRICS markets. International Journal of Emerging Markets, 10(3), 311–328. https://doi.org/10.1108/IJoEM-04-2013-0063.
• Laopodis, N. T. (2006). Dynamic Interactions among the Stock Market and Economic Activity. The Financial Review, 41(April 2005), 513–545.
• Li, K. X., Xiao, Y., Chen, S. L., Zhang, W., Du, Y., & Shi, W. (2018). Dynamics and Interdependencies Among Different Shipping Freight Markets. Maritime Policy and Management, 45(7), 837–849. https://doi.org/10.1080/03088839.2018.1488187.
• Li, T., Xue, L., Chen, Y., Chen, F., Miao, Y., Shao, X., & Zhang, C. (2018). Insights from multifractality analysis of tanker freight market volatility with common external factor of crude oil price. Physica A: Statistical Mechanics and Its Applications, 505, 374–384. https://doi.org/10.1016/j.physa.2018.02.107.
• Makridakis, S., Merikas, A., Merika, A., Tsionas, M. G., & Izzeldin, M. (2020). A novel forecasting model for the Baltic dry index utilizing optimal squeezing. Journal of Forecasting, 39(1), 56–68. https://doi.org/10.1002/for.2613.
• Mensi, W., Hammoudeh, S., Reboredo, J. C., & Nguyen, D. K. (2014). Do global factors impact BRICS stock markets? A quantile regression approach. Emerging Markets Review, 19, 1–17. https://doi.org/10.1016/j.ememar.2014.04.002.
• Mensi, W., Rehman, M. U., Maitra, D., Al-Yahyaee, K. H., & Vo, X. V. (2021). Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain. Resources Policy, 72(January), 102062. https://doi.org/10.1016/j.resourpol.2021.102062.
• Mensi, W., Shahzad, S. J. H., Hammoudeh, S., Zeitun, R., & Rehman, M. U. (2017). Diversification potential of Asian frontier, BRIC emerging and major developed stock markets: A wavelet-based value at risk approach. Emerging Markets Review, 32, 130–147. https://doi.org/10.1016/j.ememar.2017.06.002.
• Misra, P. (2018). An investigation of the macroeconomic factors affecting the Indian stock market. Australasian Accounting, Business and Finance Journal, 12(2), 71–86. https://doi.org/10.14453/aabfj.v12i2.5.
• Mohammed, B. A., & Rostam, B. (2016). Relationship between stock prices and exchange rates: Evidence from Bangladesh. International Review of Social Sciences, 4(12), 639–651. http://www.ccsenet.org/journal/index.php/ijbm/article/view/1261.
• Mohapatra, S. M., & Rath, B. N. (2015). Do macroeconomic factors matter for stock prices in emerging countries? Evidence from panel cointegration and panel causality. International Journal of Sustainable Economy, 7(2), 140–154. https://doi.org/10.1504/ijse.2015.068678.
• Naik, P., & Padhi, P. (2014). Examining the relationship between Trading Volume and Equity Market Volatility: Evidence from BRIC Countries. Global Conference on Business & Finance …, August, 1–22. http://www.researchgate.net/profile/Pramod_Naik2/publication/256945615_Examining_the_relationship_between_Trading_Volume_and_Equity_Market_Volatility_Evidence_from_BRIC_Countries/links/02e7e524170c739bd7000000.pdf.
• Nasr, A. Ben, Cunado, J., Demirer, R., & Gupta, R. (2018). Country risk ratings and stock market returns in Brazil, Russia, India, and China (Brics) countries: A nonlinear dynamic approach. Risks, 6(3). https://doi.org/10.3390/risks6030094.
• Neuhierl, A., & Weber, M. (2019). Monetary policy communication, policy slope, and the stock market. Journal of Monetary Economics, 108, 140–155. https://doi.org/10.1016/j.jmoneco.2019.08.005.
• O’Neill, J. (2010). Crude Oil price Shocks to Stock Market : Evaluating the BRICs Case. SSRN Electronic Journal, July 2009, 1–16.
• Oomen, J. G. M. (2012). The Baltic Dry Index: A predictor of stock market returns? [Tilnurg University]. http://arno.uvt.nl/show.cgi?fid=126903.
• Patel, S. (2012). The effect of Macroeconomic Determinants on the Performance of the Indian Stock Market. In G. Kalyanaram (Ed.), Global Markets and Workforce (Vol. 4, Issue 1, p. 3). NMIMS Management Review. https://doi.org/10.1049/em:19940108.
• Pershin, V., Molero, J. C., & de Gracia, F. P. (2016). Exploring the oil prices and exchange rates nexus in some African economies. Journal of Policy Modeling, 38(1), 166–180. https://doi.org/10.1016/j.jpolmod.2015.11.001.
• Pierdzioch, C., Döpke, J., & Hartmann, D. (2008). Forecasting stock market volatility with macroeconomic variables in real-time. Journal of Economics and Business, 60(3), 256–276. https://doi.org/10.1016/j.jeconbus.2007.03.001.
• Poulakidas, A., & Joutz, F. (2009). Exploring the link between oil prices and tanker rates. Maritime Policy and Management, 36(3), 215–233. https://doi.org/10.1080/03088830902861094.
• Ran, J. (2020). Inference of the US and Chinese Stock Markets Using Statistical and Computational Methods. ACM International Conference Proceeding Series, 300–309. https://doi.org/10.1145/3377571.3377626.
• Ruan, Q., Wang, Y., Lu, X., & Qin, J. (2016). Cross-correlations between Baltic Dry Index and crude oil prices. Physica A: Statistical Mechanics and Its Applications, 453(xxxx), 278–289. https://doi.org/10.1016/j.physa.2016.02.018.
• Sarıtaş, H., & Nazlıoğlu, E. H. (2019). Korku Endeksi, Hisse Senedi Piyasası ve Döviz Kuru İlişkisi: Türkiye İçin Ampirik Bir Analiz [ Fear Index, Stock Market and Exchange Rates Nexus: An Empirical Analysis for Turkey]. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 12(4), 542–551. https://doi.org/10.25287/ohuiibf.538592.
• Shackman, J., Dai, Q., Schumacher-Dowell, B., & Tobin, J. (2021). The interrelationship between ocean, rail, truck, and air freight rates. Maritime Business Review, 6(3), 256–267. https://doi.org/10.1108/MABR-08-2020-0047.
• Siddiqui, A. W., & Basu, R. (2020). An empirical analysis of relationships between cyclical components of oil price and tanker freight rates. Energy, 200, 117494. https://doi.org/10.1016/j.energy.2020.117494.
• Singh, D., Theivanayaki, M., & Ganeshwari, M. (2021). Examining Volatility Spillover Between Foreign Exchange Markets and Stock Markets of Countries such as BRICS Countries. Global Business Review. https://doi.org/10.1177/09721509211020543.
• Sorokina, A. (2013). Macroeconomic Determinants of Stock Market Behavior: Evidence from BRIC [New York University]. In SSRN Electronic Journal (Issue May). https://doi.org/10.2139/ssrn.2721034.
• Su, C. W., Huang, S. W., Qin, M., & Umar, M. (2021). Does crude oil price stimulate economic policy uncertainty in BRICS? Pacific Basin Finance Journal, 66(February), 101519. https://doi.org/10.1016/j.pacfin.2021.101519.
• Sun, X., Tang, L., Yang, Y., Wu, D., & Li, J. (2014a). Identifying the dynamic relationship between tanker freight rates and oil prices: In the perspective of multiscale relevance. Economic Modelling, 42, 287–295. https://doi.org/10.1016/j.econmod.2014.06.019.
• Sun, X., Tang, L., Yang, Y., Wu, D., & Li, J. (2014b). Identifying The Dynamic Relationship Between Tanker Freight Rates And Oil Prices: In The Perspective of Multiscale Relevance. Economic Modelling, 42, 287–295. https://doi.org/10.1016/j.econmod.2014.06.019.
• Tarı, R., Abasız, T., & Pehlivanoğlu, F. (2012). TEFE (ÜFE) -TÜFE Fiyat Endeksleri Arasındaki Nedensellik İlişkisi: Frekans Alanı Yaklaşımı [Causality Relationship between the TEFE and TUFE: A Frequency Domain Approach]. Akdeniz İ.İ.B.F Dergisi, 24, 1–15.
• Tiwari, A. K., Trabelsi, N., Alqahtani, F., & Hammoudeh, S. (2019). Analyzing systemic risk and time-frequency quantile dependence between crude oil prices and BRICS equity markets indices: A new look. Energy Economics, 83, 445–466. https://doi.org/10.1016/j.eneco.2019.07.014.
• Tripathi, V., & Kumar, A. (2015a). Do Macroeconomic Variables Affect Stock Returns in BRICS Markets? An ARDL Approach. Journal of Commerce and Accounting Research, 4(2). https://doi.org/10.21863/jcar/2015.4.2.008.
• Tripathi, V., & Kumar, A. (2015b). Relationship between Macroeconomic Factors and Aggregate Stock Returns in BRICS Stock Markets – A Panel Data Analysis. New Age Business Strategies in Emerging Global Markets, November, 104–123.
• Tripathy, N. (2022). Long memory and volatility persistence across BRICS stock markets. Research in International Business and Finance, 63(September), 101782. https://doi.org/10.1016/j.ribaf.2022.101782.
• Vanita, T., & Khushboo, A. (2015). Long run co-integrating relationship between exchange rate and stock prices: Empirical evidence from BRICS countries. In Advances in Management (Vol. 8, Issue 1, pp. 15–25).
• Wong, W.-K., Khan, H., & Du, J. (2005). Money, Interest Rate, and Stock Prices: New Evidence from Singapore and the United States. In SSRN Electronic Journal (007/2005; Issue 007). https://doi.org/10.2139/ssrn.1607605.
• Yang, J., Ge, Y. E., & Li, K. X. (2022). Measuring volatility spillover effects in dry bulk shipping market. Transport Policy, 125(December 2020), 37–47. https://doi.org/10.1016/j.tranpol.2022.01.018.
• Yang, Y., Liu, C., Sun, X., & Li, J. (2015). Spillover Effect of International Crude Oil Market on Tanker Market. International Journal of Global Energy Issues, 38(4–6), 257–277. https://doi.org/10.1504/IJGEI.2015.070270.
• Zhao, Z. Y., Chen, Y. L., & Chang, R. D. (2016). How to stimulate renewable energy power generation effectively? - China’s incentive approaches and lessons. Renewable Energy, 92, 147–156. https://doi.org/10.1016/j.renene.2016.02.001.
• Zhu, H., Huang, X., Ye, F., & Li, S. (2024). Frequency spillover effects and cross-quantile dependence between crude oil and stock markets: Evidence from BRICS and G7 countries. North American Journal of Economics and Finance, 70(November 2023), 102062. https://doi.org/10.1016/j.najef.2023.102062.
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