Korea Trade Review (무역학회지)

Korea Trade Research Association (한국무역학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Spillover Effect of Information between Factors Related to Steel Materials and BCI

제철원료 관련 요인과 BCI 간의 정보전이 효과에 관한 연구

  • Yo-Pyung Hwang (Department of Trade and Logistics, Chung-Ang University) ;
  • Ye-Eun Oh (Department of Trade and Logistics, Chung-Ang University) ;
  • Keun-Sik Park (Department of International Logistics, Chung-Ang University)
  • 황요평 (중앙대학교 무역물류학과) ;
  • 오예은 (중앙대학교 무역물류학과) ;
  • 박근식 (중앙대학교 국제물류학과)
  • Received : 2022.03.31
  • Accepted : 2022.04.26
  • Published : 2022.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The Baltic Capesize Index (BCI), which is used as an indicator for marine transportation of steel raw materials, is one of the key economic indexes for managing the risk of loss due to rapid market fluctuations when steel companies establish business strategies and procuring plans for raw materials. Still, the conditions of supply and demand of steel raw materials has been extremely affected by volatility shocks from drastic events like the financial crisis such as the Lehman Brothers incident and changes in the external environment such as COVID-19. And, especially since the 2008 financial crisis, endeavors to predict the market conditions of the steel raw material is becoming more and more arduous for the deepening uncertainty and increased volatility of BCI, which has been used as a leading indicator of the real economy. This study investigates the correlation between the steel raw material market and the marine transportation market by estimating the spillover effect of information between markets. The vector error correction model (VECM) was used to analyze information transfer based on the correlation between the BCI and crude steel production, capesize fleet supply, raw material price, and cargo volume.

Keywords

Acknowledgement

This research was supported by the 4th Educational Training Program for the Shipping, Port and Logistics from the Ministry of Ocean and Fisheries.

References

  1. AcIk, A. & S. O. BaSer (2020), "Risk and Volatility Spillover Between Commodity and Freight Markets: An Application on Capesize Freight Market", Kocaeli universitesi Sosyal Bilimler Dergisi.
  2. AcIk, A. & S. O. BaSer (2021a), "Agent Based Interaction of Commodity Price and Freight Market. Business & Management Studies", An International Journal, 9(1), 56-75.
  3. AcIk, A. & S. O. BaSer (2021b), "Interaction between Commodity Prices and Freight Rates: Content Analysis of the Dry Bulk Market Reports", Izmir Sosyal Bilimler Dergisi.
  4. Acik, A., E. Baran Kasapoglu and I. Ayaz (2021), "Information Flow between Revenue and Stock Exchanges: An Empirical Research on Liner Shipping Companies", Journal of Sustainable Development of Transport and Logistics, 6(1), 81-89.
  5. Ahn, Young-Gyun, Min-Kyu Lee (2018), "Factor Analysis Affecting on the Charterage of Capesize Bulk Carriers", Korea Trade Review, 43(3), 125-145. https://doi.org/10.22659/KTRA.2018.43.3.125
  6. Bae, Sung-Hoon., Gun-Woo Lee, & Park, Keun-Sik (2021), "A Baltic Dry Index Prediction Using Deep Learning Models", Journal of Korea Trade, 25(4), 17-36. https://doi.org/10.35611/jkt.2021.25.4.17
  7. Bae, Sung-Hoon, Keun-Sik Park (2019), "Analysis of Causality of Baltic Drybulk Index (BDI) And Maritime Trade Volum", Korea Trade Review, 44(2), 127-141. https://doi.org/10.22659/KTRA.2019.44.2.127
  8. Batchelor, R. A., A. H. Alizadeh & I. D. Visvikis (2005), "The Relation between Bid-ask Spreads and Price Volatility in Forward Markets. Deri-vatives Use", Trading & Regulation, 11(2), 105-125.
  9. Batchelor, R., A. Alizadeh, & I. Visvikis (2007), "Forecasting Spot and Forward Prices in the International Freight Market", International Journal of Forecasting, 23(1), 101-114. https://doi.org/10.1016/j.ijforecast.2006.07.004
  10. Batrinca, G. & G. Cojanu (2013), "The Dynamics of the Dry Bulk Sub-Markets. Descrierea CIP/Description of CIP-Biblioteca Nationala a Romaniei Conferinta Internationala Educatie si Creativitate pentru o Societate Bazata pe Cunoastere-STIINTE ECONOMICE", 16.
  11. Bildirici, M. E., F. Kayikci & I. S. Onat (2015), "Baltic Dry Index as a Major Economic Policy Indicator: the Relationship with Economic Growth", Procedia-Social and Behavioral Sciences, 210, 416-424. https://doi.org/10.1016/j.sbspro.2015.11.389
  12. Chen, F., K. Tian, X. Ding, Y. Miao & C. Lu (2016), "Finite-size Effect and the Components of Multifractality in Transport Economics Volatility Based on Multifractal Detrending Moving Average Method", Physica A: Statistical Mechanics and its Applications, 462, 1058-1066. https://doi.org/10.1016/j.physa.2016.06.101
  13. Chen, J. & M. G. Abdullah (2019), "Research and Analysis of International Shipping Market Freight Index. In CICTP 2019", 34-42.
  14. Chen, S., H. Meersman & E. v. d. Voorde (2012), "Forecasting Spot Rates at Main Routes in the Dry Bulk Market", Maritime Economics & Logistics, 14(4), 498-537. https://doi.org/10.1057/mel.2012.18
  15. Chou, C. C. and K. S. Lin (2018), "A Fuzzy Neural Network Combined with Technical Indicators and Its Application to Baltic Dry Index Forecasting", Journal of Marine Engineering & Technology, 18(2), 82-91. https://doi.org/10.1080/20464177.2018.1495886
  16. Chung, Sang-Kuck, Seong-Ki Kim (2011), "A Study on the Effect of Changes in Oil Price on Dry Bulk Freight Rates and Interrelations between Dry Bulk Freight Rates", Journal of Korea Port Economic Association, 27(2), 217-240.
  17. Dai, S., F. Chen, Y. Zeng and X. Zeng (2016), "Scaling Behavior of Bulk Freight Rate Volatility before and after Noise Reduction", Journal of Shanghai Jiaotong University (Science), 21(6), 655-661. https://doi.org/10.1007/s12204-016-1778-0
  18. Dave, E., A. Leonardo, M. Jeanice and N. Hanafiah (2021), "Fore-casting Indonesia Exports Using a Hybrid Model ARIMA- LSTM", Procedia Computer Science, 179, 480-487. https://doi.org/10.1016/j.procs.2021.01.031
  19. Dbouk, W. and I. Jamali (2018), "Predicting Daily Oil Prices: Linear and Non-linear Models". Research in International Business and Finance, 46, 149- 165. https://doi.org/10.1016/j.ribaf.2018.01.003
  20. Ding, S., Y. Zhang and M. Duygun (2019), "Modeling Price Volatility Based on a Genetic Programming Approach", British Journal of Management, 30(2), 328-340. https://doi.org/10.1111/1467-8551.12359
  21. Ding, X., S. Dai, F. Chen, Y. Miao, K. Tian, Y. Zeng, H. Xu and C. Qin (2017), "Long Memory and Scaling Behavior Study of Bulk Freight Rate Volatility with Structural Breaks", Transportation Letters, 10(6), 343-353.
  22. Drobetz, W., T. Richter and M. Wambach (2012), "Dynamics of Time-varying Volatility in the Dry Bulk and Tanker Freight Markets", Applied Financial Economics, 22(16), 1367-1384. https://doi.org/10.1080/09603107.2012.657349
  23. Engle, R. F. (1982), "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation", Econometrica: Journal of the Econometric Society, 987-1007.
  24. Engle, R. F. and T. Bollerslev (1986), "Modelling the Persistence of Conditional Variances", Econometric Reviews, 5(1), 1-50.
  25. Engle, R. F. and K. Sheppard (2001), "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH", NBER Working Papers, No. 8554, 2001, 4-18.
  26. Fan, Y. H., Y. W. Xing and H. L. Yang (2014), "Prediction of Baltic Capesize Freight Index Based on GARCH Model", Applied Mechanics and Materials, 488, 1494-1497.
  27. Goulas, L., & G. Skiadopoulos (2012), "Are Freight Futures Markets Efficient? Evidence from IMAREX", International Journal of Forecasting, 28(3), 644-659. https://doi.org/10.1016/j.ijforecast.2011.11.004
  28. Gu, Yimiao, Xiaoxu Dong, and Zhenxi Chen (2020) "The Relation between the International and China Shipping Markets", Research in Transportation Business & Management, 100427.
  29. Gu, Yimiao, Zhenxi Chen, and Qingyang Gu (2021), "Determinants and International Influences of the Chinese Freight Market", Empirical Economics, 1-18.
  30. Gu, Yimiao, Zhenxi Chen & D. Lien (2019), "Baltic Dry Index and Iron Ore Spot Market: Dynamics and Interactions", Applied Economics, 51(35), 3855-3863. https://doi.org/10.1080/00036846.2019.1584384
  31. Guan, F., Z. Peng, K. Wang, X. Song & J. Gao (2016), "Multi-Step Hybrid Prediction Model of Baltic Supermax Index Based on Support Vector Machine", Neural Network World, 26(3), 219-232. https://doi.org/10.14311/NNW.2016.26.012
  32. Ha, J. Y. & Y. Shin (2021), "Lead-lag Relationship between the Shipping Freight Rate and Agricultural Commodity Import Price in Korea", Journal of Navigation and Port Research, 45(2), 69-74. https://doi.org/10.5394/KINPR.2021.45.2.069
  33. Hsiao, Y.J., H.C. Chou and C.C. Wu (2013), "Return lead-lag and Volatility Ttransmission in Shipping Freight Markets", Maritime Policy & Management, 41(7), 697-714.
  34. Jeon, Bong-Gil, Jin-Ho Oh, Keun-Sik Park (2020), "A Study of Correlation Between China Iron Ore Import, Steel Export Activity and Dry Bulk Index: Focus on Capesize C5/C10/C14 and Supramax S2/S3", Journal of Korea Port Economic Association, 36(3), 115-136. https://doi.org/10.38121/kpea.2020.09.36.3.115
  35. Jeon, J., E. Gulay and O. Duru (2021), "Spectral Analysis of the Dry Bulk Shipping Market by Utilizing the System Dynamics Approach", Maritime Business Review, 1-16.
  36. Kamal, I. M., Hye-Rim Bae, Sung-Hyun Sim, Hye-Mee Kim, Do-Hee Kim, Yu-Lim Choi, & Hee-Sung Yun (2019), "Forecasting High-Dimensional Multivariate Regression of Baltic Dry Index (BDI) using Deep Neural Networks (DNN)", ICIC Express Lett, 13, 427-434.
  37. Katris, C. & M. G. Kavussanos (2021), "Time Series Forecasting Methods for the Baltic Dry Index", Journal of Forecasting, 40(8), 1540-1565. https://doi.org/10.1002/for.2780
  38. Kavussanos, M. G. & N. K. Nomikos (2000), "Constant vs. Time- varying Hedge Ratios and Hedging Efficiency in the BIFFEX market", Transportation Research Part E: Logistics and Transportation Review, 36(4), 229-248. https://doi.org/10.1016/S1366-5545(99)00029-0
  39. Kavussanos, M. G. & I. D. Visvikis (2004), "Market Interactions in Returns and Volatilities between Spot and Forward Shipping Freight Markets", Journal of Banking & Finance, 28(8), 2015- 2049. https://doi.org/10.1016/j.jbankfin.2003.07.004
  40. Kavussanos, M. G. & I. D. Visvikis (2011), "Shipping Freight Derivatives: A Survey of Recent Evidence", Maritime Policy & Management, 33(3), 233-255. https://doi.org/10.1080/03088830600783152
  41. Kavussanos, M. G., I. D. Visvikis and R. A. Batchelor (2004), "Over-the-counter forward Contracts and Spot Price Volatility in Shipping", Transportation Research Part E: Logistics and Transportation Review, 40(4), 273-296. https://doi.org/10.1016/j.tre.2003.08.007
  42. Kavussanos, M. G., I. D. Visvikis & D. N. Dimitrakopoulos (2014), "Economic Spillovers between Related Derivatives Markets: The Case of Commodity and Freight Markets", Transportation Research Part E: Logistics and Transportation Review, 68, 79-102. https://doi.org/10.1016/j.tre.2014.05.003
  43. Kavussanos, M. G., I. D. Visvikis and D. Menachof (2004), "The Unbiasedness Hypothesis in the Freight Forward Market: Evidence from Cointegration Tests", Review of Derivatives Research, 7(3), 241-266. https://doi.org/10.1007/s11147-004-4811-7
  44. Kim, Chang-Beom (2016), "Impact of Exchange Rate Movements, Global Economic Activity, and the BDI Volatility on Loaded Port Cargo throughput in South Korea", The Asian Journal of Shipping and Logistics, 32(4), 243-248. https://doi.org/10.1016/j.ajsl.2016.12.008
  45. Kim, Chang-Beom (2018), "Leverage Effect of HRCI Volatility and the Volatility Impact on Korean Export Container Volume before and after the Global Financial Crisis: Application of ARIMA-EGARCH and GIRF", The Asian Journal of Shipping and Logistics, 34(3), 227-233. https://doi.org/10.1016/j.ajsl.2018.09.006
  46. Kim, Hyeong-Jun, Doo-Jin Ryu and Hoon Cho. (2019), "Short-term Forecasts of the Baltic Dry Index (bdi) Using Time-series Factor Decomposition", Korean Management Review, 48(3), 715-731. https://doi.org/10.17287/kmr.2019.48.3.715
  47. Ko, Byoung-Wook (2011), "Dynamics of Dry Bulk Freight Market: Through the Lens of a Common Stochastic Trend Model", The Asian Journal of Shipping and Logistics, 27(3), 387-404. https://doi.org/10.1016/S2092-5212(11)80018-0
  48. Ko, Byoung-Wook (2018), "Dynamic Patterns of Dry Bulk Freight Spot Rates through the Lens of a Time-varying Coefficient Model", Transportation Research Part A: Policy and Practice, 118, 319-330. https://doi.org/10.1016/j.tra.2018.09.017
  49. Korinek, J. & P. Sourdin (2009), "Maritime Transport Costs and Their Impact on Trade", Organization for Economic Co-operation and Development TAD/TC/WP, 7.
  50. Lee, Sung-Yhun and Ki-Myung Ahn (2018), "Study on the Forecasting and Effecting Factor of BDI by VECM", Journal of Korean Navigation and Port Research, 42(6), 546-554.
  51. Li, J., J. Hao, Q. Feng, X. Sun & M. Liu (2021), "Optimal Selection of Heterogeneous Ensemble Strategies of Time Series Forecasting with Multi-objective Programming", Expert Systems with Applications, 166.
  52. Lim, Sang-Seop, Seok-Hun Kim, & Dae-Won Kim (2021), "Analysis of Baltic Dry Bulk Index with EMD-based ANN", In Proceedings of the Korean Society of Computer Information Conference Korean Society of Computer Information, 329-330.
  53. Lin, A. J., H. Y. Chang & J. L. Hsiao (2019), "Does the Baltic Dry Index Drive Volatility Spillovers in the Commodities, Currency, or Stock Markets?", Transportation Research Part E: Logistics and Transportation Review, 127, 265-283. https://doi.org/10.1016/j.tre.2019.05.013
  54. Lin, Y. J. & C. C. Wang (2014), "The Dynamic Analysis of Baltic Exchange Dry Index", International Mathematical Forum, 9, 803-823.
  55. Makridakis, S., A. Merikas, A. Merika, M. G. Tsionas & M. Izzeldin (2019), "A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing", Journal of Forecasting, 39(1), 56-68.
  56. Michail, N. A. and K. D. Melas (2020), "Quantifying the Relationship between Seaborne Trade and Shipping Freight Rates: A Bayesian Vector Autoregressive Approach", Maritime Transport Research, 1, 100001.
  57. Moutzouris, I. C. & N. K. Nomikos (2019), "The Formation of Forward Freight Agreement Rates in Dry Bulk Shipping: Spot Rates, Risk Premia, and Heterogeneous Expectations", Journal of Futures Markets, 39(8), 1008- 1031.
  58. Munim, Z. H. & H. J. Schramm (2016), "Forecasting Container Shipping Freight Rates for the Far East - Northern Europe Trade Lane", Maritime Economics & Logistics, 19(1), 106-125. https://doi.org/10.1057/s41278-016-0051-7
  59. Munim, Z. H. & H. J. Schramm (2020), "Forecasting Container Freight Rates for Major Trade Routes: a Comparison of Artificial Neural Networks and Conventional Models", Maritime Economics & Logistics, 23(2), 310-327. https://doi.org/10.1057/s41278-020-00156-5
  60. Nelson, D. B. (1991), "Conditional Heteroskedasticity in Asset Returns: A New Approach", Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260
  61. Nomikos, N. K. & K. Doctor (2013), "Economic Significance of Market Timing Rules in the Forward Freight Agreement Markets", Transportation Research Part E: Logistics and Transportation Review, 52, 77-93. https://doi.org/10.1016/j.tre.2012.11.009
  62. Papailias, F., D. D. Thomakos & J. Liu (2017), "The Baltic Dry Index: Cyclicalities, Forecasting and Hedging Strategies", Empirical Economics, 52(1), 255-282. https://doi.org/10.1007/s00181-016-1081-9
  63. Pelagidis, T. & I. Karaoulanis (2021), "Capesize Markets Behavior: Explaining Volatility and Expectations", The Asian Journal of Shipping and Logistics, 37(1), 82-90. https://doi.org/10.1016/j.ajsl.2020.08.001
  64. Pimic, M., M. Joksimovic, A. Muhovic and N. Radivojevic (2021), "Examining the Impact of Movements of the Commodity Price on the Value of the Baltic Dry Index during Covid19 Pandemic", Asian Journal of Economics and Empirical Research, 8(2), 67-72.
  65. Pourkermani, K. (2012), Essays on the Econometric Modelling and Forecasting of Shipping Market Variables, Doctoral dissertation, Newcastle University.
  66. RAJU, T. B. (2019), "Understanding Relationships between Charter Rates and Ship Building Prices in Liquefied Natural Gas Shipping Markets", Editorial Board.
  67. Sahoo, S. R. (2018), Essays on Derivatives and Risk Management on Freight and Commodity: An Attempt to Anticipate and Hedge the Market Volatilities, Doctoral dissertation, University of Reading.
  68. Sartorius, Kurt, Benn Sartorius, and Dino Zuccollo (2018), "Does the Baltic Dry Index predict economic activity in South Africa? A review from 1985 to 2016", South African Journal of Economic and Management Sciences, 21(1), 1-9.
  69. Syriopoulos, T., M. Tsatsaronis & I. Karamanos (2020), "Support Vector Machine Algorithms: An Application to Ship Price Forecasting", Computational Economics, 57(1), 55-87. https://doi.org/10.1007/s10614-020-10032-2
  70. Taib, C. M. I. C., & Z. I. Mohtar (2018), "Forecasting Spot Freight Rates Using Vector Error Correction Model in the Dry Bulk Market", Malaysian Journal of Mathematical Sciences, 12(2), 235-253.
  71. Tsioumas, V. & S. Papadimitriou (2018), "The Dynamic Relationship between Freight Markets and Commodity Prices Revealed", Maritime Economics & Logistics, 20(2), 267-279. https://doi.org/10.1057/s41278-016-0005-0
  72. Tsioumas, V., S. Papadimitriou, Y. Smirlis & S. Z. Zahran (2017), "A Novel Approach to Forecasting the Bulk Freight Market", The Asian Journal of Shipping and Logistics, 33(1), 33-41. https://doi.org/10.1016/j.ajsl.2017.03.005
  73. Tsouknidis, D. A. (2016), "Dynamic Volatility Spillovers across Shipping Freight Markets", Transportation Research Part E: Logistics and Transportation Review, 91, 90-111. https://doi.org/10.1016/j.tre.2016.04.001
  74. Uyar, K., u. Ilhan and A. Ilhan (2016), "Long Term Dry Cargo Freight Rates Forecasting by Using Recurrent Fuzzy Neural Networks", Procedia Computer Science, 102, 642-647. https://doi.org/10.1016/j.procs.2016.09.455
  75. Visvikis, I. D. (2002), An Econometric Analysis of the Forward Freight Market, Doctoral dissertation, City University London.
  76. Yang, Z. & E. E. Mehmed (2019), "Artificial Neural Networks in Freight Rate Forecasting", Maritime Economics & Logistics, 21(3), 390-414. https://doi.org/10.1057/s41278-019-00121-x
  77. Yildiz, B., & U. Bucak (2018), "Determinants of Freight Rates: A Study on the Baltic Dry Index", Istanbul Gelisim universitesi Sosyal Bilimler Dergisi, 4(2), 17-32.
  78. Yin, J., M. Luo & L. Fan (2016), "Dynamics and Interactions between Spot and Forward Freights in the Dry Bulk Shipping Market", Maritime Policy & Management, 44(2), 271-288.
  79. Yin, J., M. Luo & L. Fan (2017), "Dynamics and Interactions between Spot and Forward Freights in the Dry Bulk Shipping Market", Maritime Policy & Management, 44(2), 271-288. https://doi.org/10.1080/03088839.2016.1253884
  80. Yu, C. K. W., T. L. Yip & S. K. Choy (2019), "Optimal Portfolio Choice for Ship Leasing Investments", Maritime Policy & Management, 46(7), 884- 900. https://doi.org/10.1080/03088839.2019.1647361
  81. Zeng, Q. & C. Qu (2014), "An Approach for Baltic Dry Index Analysis Based on Empirical Mode Decomposition", Maritime Policy & Management, 41(3), 224-240. https://doi.org/10.1080/03088839.2013.839512
  82. Zeng, Q., C. Qu, A. K. Y. Ng & X. Zhao (2015), "A New Approach for Baltic Dry Index Forecasting Based on Empirical Mode Decomposition and Neural Networks", Maritime Economics & Logistics, 18(2), 192-210. https://doi.org/10.1057/mel.2015.2
  83. Zhang, H. & Q. Zeng (2015), "A Study of the Relationships between the Time Charter and Spot Freight Rates", Applied Economics, 47(9), 955-965. https://doi.org/10.1080/00036846.2014.985371
  84. Zhang, J., Q. Zeng, & X. Zhao (2014). "Forecasting Spot Freight Rates Based on Forward Freight Agreement and Tme Charter Contract", Applied Economics, 1-10.
  85. Zhang, X., M. Y. Chen, M. G. Wang, Y. E. Ge and H. E. Stanley (2019), "A Novel Hybrid Approach to Baltic Dry Index Forecasting Based on a Combined Dynamic Fluctuation Network and Artificial Intelligence Method", Applied Mathematics and Computation, 361, 499-516. https://doi.org/10.1016/j.amc.2019.05.043
  86. Zhang, X., T. Xue and H. Eugene Stanley (2019), "Comparison of Econometric Models and Artificial Neural Networks Algorithms for the Prediction of Baltic Dry Index", IEEE, 7, 1647-1657.