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Volatility Spillover Effects between BDI with CCFI and SCFI Shipping Freight Indices

BDI와 CCFI 및 BDI와 SCFI 운임지수 사이의 변동성 파급 효과

  • 이몽화 (동국대학교 국제통상학과 ) ;
  • 김석태 (동국대학교 국제통상학과)
  • Received : 2023.01.17
  • Accepted : 2023.02.27
  • Published : 2023.02.28
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Abstract

The objective of this study is to investigate the volatility spillover effects among BDI, CCFI and SCFI. This paper will divide the empirical analysis section into two periods to analyze and compare the differences in volatility spillover effect between shipping freight indices before and after the outbreak of COVID-19 separately. First, in order to compare the mean spillover impact and index lead-lag correlations in BDI and CCFI indices, along with BDI and SCFI indices before and after COVID-19, the co-integration analysis and the test of Granger causality built on the VAR model were utilized. Second, the impulse response and variance decomposition are employed in this work to investigate how the shipping freight index responds to shocks experienced by itself and other freight indices in a short period. Before the COVID-19 epidemic, the results demonstrated that the BDI freight index is the Granger cause of the variable CCFI freight index. But the BDI and CCFI freight indices have no apparent lead-lag relationships after COVID-19, and this empirical result echoes the cointegration test result. After the COVID-19 epidemic, the SCFI index leads the BDI index. This study employs the VAR-BEKK-GARCH joint model to explore the volatility spillover results between dry bulk and container transport markets before and after COVID-19. The empirical results demonstrate that after COVID-19, fluctuations in the BDI index still affect the CCFI index in the maritime market. However, there is no proof of a volatility spillover relationship between the BDI and SCFI after the COVID-19 epidemic. This study will provide an insight into the volatility relationship among BDI, CCFI and SCFI before and after the the COVID-19 epidemic occurred.

본문에서는 실증분석 부분을 두 시기로 나누어 COVID-19 전후에 해운지수 간의 변동성 파급효과 차이를 비교 분석하고자 하였다. 코로나19 전후에 해운지수 간의 평균 파급효과 및 지수 관계를 비교하기 위해 VAR 모델에 구축된 공적분 분석과 Granger 인과관계 테스트를 활용하였다. 또한, 본 연구에서는 해운지수가 단기적으로 자신의 충격에 대한 반응과 한 지수가 다른 지수에 대한 충격을어떻게 반영하는지 밝히기 위해서 충격반응함수 및 예측 오차 분산분해를 활용하였다. COVID-19 전염병 이전에는 BDI 해운지수가 CCFI 해운지수에 미치는 관계가 존재하지만 COVID-19 이후에는 BDI지수와 CCFI지수 사이에 뚜렷한 lead-lag 관계가 없다는 것으로 나타났다. COVID-19 전염병 이전에는 BDI지수는 SCFI지수의 변화를 설명하고 있고, 코로나19 확산 이후에는 SCFI 지수가 BDI 지수를 앞서고 있다는 것을 보여주고 있다. 또한 VAR-BEKK-GARCH 모델을 활용하여 COVID-19 전후 벌크 화물 해운시장 및 컨테이너 해운시장 간의 변동성 파급효과를 분석하였을 때 코로나19 이전의 BDI지수는 CCFI지수와 SCFI 지수에 대한 단발성 변동성 파급효과를 보였고 COVID-19 이후에도 BDI 지수의 변동성이 CCFI 지수에 여전히 영향을 미친다는 것을 보여준다. 하지만 코로나19 확산 이후에는 BDI지수와 SCFI지수 간의 변동성 파급 관계가 존재하지 않는 것으로 나타났다.

Keywords

References

  1. Abdullah, A. C. I. K. (2019), "Volatility spillover from global container freight indices to port throughputs in Turkey", Uluslararasi Ekonomi ve Yenilik Dergisi, 5(1), 101-113. https://doi.org/10.20979/ueyd.519545
  2. Alexandridis, G., M. G. Kavussanos., C. Y. Kim., D. A. Tsouknidis and I. D. Visvikis (2018), "A survey of shipping finance research: Setting the future research agenda". Transportation Research Part E: Logistics and Transportation Review, 115, 164-212. https://doi.org/10.1016/j.tre.2018.04.001
  3. Alizadeh, A. H. (2013), "Trading volume and volatility in the shipping forward freight market". Transportation Research Part E: Logistics and Transportation Review, 49(1), 250-265. https://doi.org/10.1016/j.tre.2012.08.001
  4. Alizadeh-Masoodian, A. H. (2001), "An Econometric Analysis of the Dry Bulk Shipping Industry: Seasonality, Market Efficiency and Risk Premia".
  5. Alphaliner. (2007), "Containership Charter Rates vs. Bulk Carrier Charter Rates". Alphaliner - The Worldwide Reference in Liner Shipping.
  6. Acik, A., E. B. Kasapoglu and I. S. 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. https://doi.org/10.14254/jsdtl.2021.6-1.7
  7. Bae, S. H. and K. S. Park (2019), "Analysis of causality of Baltic Drybulk index (BDI) and maritime trade volume". Korea Trade Review, 44(2), 127-141. https://doi.org/10.22659/KTRA.2019.44.2.127
  8. Bildirici, M. E., F. Kayikci and 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
  9. Bollerslev, T., R. Y. Chou and K. F. Kroner (1992), "ARCH modeling in finance: A review of the theory and empirical evidence". Journal of econometrics, 52(1-2), 5-59. https://doi.org/10.1016/0304-4076(92)90064-X
  10. Campbell, J. Y. (1991), "A variance decomposition for stock returns". The economic journal, 101(405), 157-179. https://doi.org/10.2307/2233809
  11. Chen, S., H. Meersman and E. Van de Voorde (2010), "Dynamic interrelationships in returns and volatilities between Capesize and Panamax markets". Maritime Economics & Logistics, 12(1), 65-90.
  12. Choi, T. M., S. H. Chung and X. Zhuo (2020), "Pricing with risk sensitive competing container shipping lines: Will risk seeking do more good than harm?". Transportation Research Part B: Methodological, 133, 210-229. https://doi.org/10.1016/j.trb.2020.01.003
  13. Diebold, F. X. and K. Yilmaz (2012), "Better to give than to receive: Predictive directional measurement of volatility spillovers". International Journal of forecasting, 28(1), 57-66. https://doi.org/10.1016/j.ijforecast.2011.02.006
  14. Diebold, F. X. and K. Yilmaz (2009), "Measuring financial asset return and volatility spillovers, with application to global equity markets". The Economic Journal, 119(534), 158-171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
  15. Drobetz, W., D. Schilling and L. Tegtmeier (2010), "Common risk factors in the returns of shipping stocks". Maritime Policy & Management, 37(2), 93-120. https://doi.org/10.1080/03088830903533726
  16. Goulielmos, A. M., C. Giziakis and A. Georgantzi (2012), "An application of non-linear methods to the prediction of future freight rates, 2006-2008". International Journal of Shipping and Transport Logistics, 4(1), 78-106. https://doi.org/10.1504/IJSTL.2012.044136
  17. Gu, Wen-Bo (2019), "Comparative analysis of international Bulk Freight Index".
  18. Han, Q., B. Yan., G. Ning and B. Yu (2014), "Forecasting dry bulk freight index with improved SVM". Mathematical Problems in Engineering, 2014.
  19. Hirschi, M. (2017), "Structural and Functional Studies of Nucleoside Transporters" (Doctoral dissertation, Duke University).
  20. Hsiao, Y. J., H. C. Chou and C. C. Wu (2014), "Return lead-lag and volatility transmission in shipping freight markets". Maritime Policy & Management, 41(7), 697-714. https://doi.org/10.1080/03088839.2013.865849
  21. Jeon, J. (2019), "A Study on the Causal Relationship Between Shipping Freight Rates". Journal of Convergence for Information Technology, 9(12), 47-53. https://doi.org/10.14801/JAITC.2019.9.1.47
  22. Jia, J. and R. Adland (2002), "An empirical analysis of the time-varying correlation of returns in international shipping". In Conference of the International Association of Maritime.
  23. Johansson, A. C. and C. Ljungwall (2009), "Spillover effects among the Greater China stock markets". World Development, 37(4), 839-851. https://doi.org/10.1016/j.worlddev.2008.07.015
  24. Jugovic, A., N. Komadina and A. Peric Hadzic (2015), "Factors influencing the formation of freight rates on maritime shipping markets". Pomorstvo, 29(1), 23-29.
  25. Kang, S. H., C. Cheong and S. M. Yoon (2013), "Intraday volatility spillovers between spot and futures indices: Evidence from the Korean stock market". Physica A: Statistical Mechanics and its Applications, 392(8), 1795-1802. https://doi.org/10.1016/j.physa.2013.01.017
  26. Karamperidis, S., E. Jackson and J. Mangan (2013), "The use of indices in the maritime transport sector". Maritime Policy & Management, 40(4), 339-350. https://doi.org/10.1080/03088839.2013.776182
  27. Kavussanos, M. G. (1996), "Comparisons of volatility in the dry-cargo ship sector: Spot versus time charters, and smaller versus larger vessels". Journal of Transport economics and Policy, 67-82.
  28. Kavussanos, M. G. (2003), "Time varying risks among segments of the tanker freight markets". Maritime Economics & Logistics, 5(3), 227-250. https://doi.org/10.1057/palgrave.mel.9100079
  29. Kavussanos, M. G. and I. D. Visvikis (2006), "Shipping freight derivatives: a survey of recent evidence". Maritime Policy & Management, 33(3), 233-255. https://doi.org/10.1080/03088830600783152
  30. Koyuncu, K. and L. TAVACIOĞLU (2021), "Forecasting Shanghai containerized freight index by using time series models". Marine Science and Technology Bulletin, 10(4), 426-434. https://doi.org/10.33714/masteb.1024663
  31. Kraft, D. F. and R. F. Engle (1982), "Autoregressive conditional heteroskedasticity in multiple time series". UCSD manuscript.
  32. Kumar, A. (2016), "Multi-party Democracy in Maldives and the Emerging Security Environment in the Indian Ocean Region". Pentagon Press.
  33. Kuo, C. C., H. C. Chou and C. C. Chang (2016), "Exploring risk-return relations in dry bulk shipping". International Journal of Shipping and Transport Logistics, 8(4), 488-506. https://doi.org/10.1504/IJSTL.2016.077291
  34. Kyriakou, I., P. K. Pouliasis., N. C. Papapostolou and N. K. Nomikos (2018), "Income uncertainty and the decision to invest in bulk shipping". European Financial Management, 24(3), 387-417. https://doi.org/10.1111/eufm.12132
  35. Lane, J. M. and M. Pretes (2020), "Maritime dependency and economic prosperity: Why access to oceanic trade matters". Marine Policy, 121, 104180.
  36. Lin, A. J., H. Y. Chang and 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
  37. Lu, Wei (2013), "Feasibility analysus of new shanghai containerized freight index futures" (Master's Thesis of Shanghai Jiaotong University).
  38. Luo, M., L. Fan and L. Liu (2009), "An econometric analysis for container shipping market". Maritime Policy & Management, 36(6), 507-523. https://doi.org/10.1080/03088830903346061
  39. M. McAleer., S. Hoti and F. Chan (2009), "Structure and asymptotic theory for multivariate asymmetric conditional volatility". Econometric Reviews, 28(5), 422-440. https://doi.org/10.1080/07474930802467217
  40. Munim, Z. H. and H. J. Schramm (2021), "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
  41. Pesaran, H. H. and Y. Shin (1998), "Generalized impulse response analysis in linear multivariate models". Economics letters, 58(1), 17-29. https://doi.org/10.1016/S0165-1765(97)00214-0
  42. Rafiq, S. (2009), "Oil consumption, pollutant emission, oil proce volatility and economic activities in selected Asian Developing Economies" (Doctoral dissertation, Curtin University).
  43. Saddam, A. and F. Kari (2014), "A long-run dynamic analysis of FDI, growth and oil export in GCC countries: An evidence from VECM model". Canadian Social Science, 10(1), 76-82.
  44. Shi, W. and Kevin-X Li (2017), "Themes and tools of maritime transport research during 2000-2014". Maritime Policy & Management, 44(2), 151-169. https://doi.org/10.1080/03088839.2016.1274833
  45. Stopford, M. (2009), "Maritime Economics", London, Routledge, p. 140.
  46. 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
  47. Wang, Gang-Jin., Yan-Ping Tang., Chi Xie and Shou Chen (2019), "Is bitcoin a safe haven or a hedging asset? Evidence from China". Journal of Management Science and Engineering, 4(3), 173-188. https://doi.org/10.1016/j.jmse.2019.09.001
  48. Wang, Jun-Peng., Yu-Bo Xue and Ming-Hao Liu (2016), "An analysis of bitcoin price based on VEC model". In 2016 international conference on economics and management innovations, 180-186. Atlantis Press.
  49. Xin, Shi (2000), "The study on the compilation of the China container freight index". Maritime Policy & Management, 27(3), 303-308. https://doi.org/10.1080/030888300411121
  50. Xu, Wei-Hang (2021), "Research on Shanghai export container freight index fluctuation based on Arma-Garch-X model".
  51. Yifei, Z., Z. Dali and T. Yanagita (2018), "Container liner freight index based on data from e-booking platforms". Maritime Policy & Management, 45(6), 739-755. https://doi.org/10.1080/03088839.2018.1443224
  52. Yin, Jing-Bo and Jin-Hao Shi (2018), "Seasonality patterns in the container shipping freight rate market." Maritime Policy & Management, 45(2), 159-173. https://doi.org/10.1080/03088839.2017.1420260
  53. Yoo, Seung-Hoon and Se-Ju Ku (2009), "Causal relationship between nuclear energy consumption and economic growth: A multi-country analysis". Energy policy, 37(5), 1905-1913. https://doi.org/10.1016/j.enpol.2009.01.012
  54. Yu, Lean., Rui Zha., D. Stafylas., Kai-Jian He and J. Liu (2020), "Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models." International Review of Financial Analysis, 68, 101280.
  55. Zhang, Shi-Xin and Li-Juan Pei (2018) "Correlation Research of Shanghai Index and the BDI." In 2017 7th International Conference on Education and Management (ICEM 2017), 653-656. Atlantis Press,.
  56. Zhang, Yue-Jun., Ying Fan., Hsien-Tang Tsai and Yi-Ming Wei (2008), "Spillover effect of US dollar exchange rate on oil prices." Journal of Policy modeling, 30(6), 973-991. https://doi.org/10.1016/j.jpolmod.2008.02.002
  57. Zolfaghari, M., H. Ghoddusi and F. Faghihian (2020), "Volatility spillovers for energy prices: a diagonal BEKK approach". Energy Economics, 92, 104965.