DOI QR코드

DOI QR Code

에너지 가격, 탱커운임지수, 불확실성 사이의 연계성 분석

Analysis of connectedness Between Energy Price, Tanker Freight Index, and Uncertainty

  • 투고 : 2022.12.09
  • 심사 : 2022.12.27
  • 발행 : 2022.12.31

초록

기술발전(셰일가스, 셰일오일), 무역전쟁, COVID-19, 러시아-우크라이나 전쟁 등으로 인해 에너지 시장의 불확실성이 확대되고 있다. 특히, 2020년 이후 COVID-19, 러시아-우크라이나 전쟁의 영향으로 장기화된 수요 감소로 인한 상품 운송의 공급체인의 변화 등으로 인해 에너지 시장의 국제적 교역에 대한 위험이 크게 증가하고 있다. 본 연구에서는 이러한 점을 고려하여 에너지 시장에서의 국제적 교역의 연계성을 파악하기 위해 에너지 가격, 탱커운임지수, 불확실성 사이의 연계성을 분석하였다. 주요 분석결과를 요약하면 다음과 같다. 첫째, MS-VAR 모형을 이용하여 에너지 가격 모형의 안정기와 불안정기를 분석한 결과 원유시장 모형과 천연가스시장 모형 모두 불안정기에 비해 안정기가 유지될 확률이 더 높게 나타나 특정 사건에 의해 변동성이 확대된다는 것을 확인할 수 있었다. 둘째, 에너지 시장의 안정기와 불안정기의 연계성 분석 결과를 살펴보면, 총 연계성의 경우 원유시장 모형과 천연가스시장 모형 모두 안정기에 비해 불안정기에 변수 간에 연계성이 확대된다는 것을 확인할 수 있었다. 에너지 시장의 안정기의 경우 연계성 정도를 고려했을 때, 수요측 요인을 대표하는 탱커 운임지수의 효과가 크다는 것을 확인하였다. 셋째, 에너지 시장의 불안정기는 원유시장 모형에 비해 천연가스 시장의 연계성이 급격하게 증가하는 것으로 보아 원유시장에 비해 에너지 가격에 영향을 미치는 불확실성이 확대되면 천연가스 시장의 변동성 전이 효과가 더 큰 것으로 나타났다.

Uncertainties in the energy market are increasing due to technology developments (shale revolution), trade wars, COVID-19, and the Russia-Ukraine war. Especially, since 2020, the risk of international trade in the energy market has increased significantly due to changes in the supply chain of transportation and due to prolonged demand reduction because of COVID-19 and the Russian-Ukraine war. Considering these points, this study analyzed connectedness between energy price, tanker index, and uncertainty to understand the connectedness between international trade in the energy market. Main results are summarized as follows. First, as a result of analyzing stable period and unstable period of the energy price model using the MS-VAR model, it was confirmed that both the crude oil market model and the natural gas market model had a higher probability of maintaining stable period than unstable period, increasing volatility by specific events. Second, looking at the results of the analysis of the connectedness between stable period and unstable period of the energy market, it was confirmed that in the case of total connectedness, connectedness between variables was increased in the unstable period compared to the stable period. In the case of the energy market stable period, considering the degree of connectedness, it was confirmed that the effect of the tanker freight index, which represents the demand-side factor, was significant. Third, unstable period of the natural gas market model increases rapidly compared to the crude oil market model, indicating that the volatility spillover effect of the natural gas market is greater when uncertainties affecting energy prices increase compared to the crude oil market.

키워드

과제정보

본 연구는 2020년 대한민국 교육부와 한국연구재단(NRF-2020S1A5B8103268) 연구비와 2021-2022학년도 부산대학교 BK21 FOUR 대학원혁신지원사업의 지원을 받아 수행된 연구임

참고문헌

  1. 김명직.장국현(2013), 「금융시계열분석」, 경문사.
  2. 김명희(2022), 해상운임에 영향을 미치는 주요 요인에 관한 연구, 한국항해항만학회지, 제46집 4호, 385-391. https://doi.org/10.5394/KINPR.2022.46.4.385
  3. 김부권.김동윤.최기홍(2019), 국제운임지수와 원유가격의 의존관계 분석, 한국항만경제학회지, 제35집 4호, 107-120.
  4. 김부권.김동윤.최기홍(2020), 국제 해운 운임지수와 미국과 중국의 무역 불확실성 사이의 의존성 구조분석, 한국항만경제학회지, 36집 제4호, 93-106.
  5. 김부권.최기홍.윤성민(2020), Copula 모형을 이용한 에너지 가격과 경제적 불확실성 사이의 의존관계분석, 자원환경경제연구, 제29집 2호, 145-170. https://doi.org/10.15266/KEREA.2020.29.2.145
  6. 임상석.김석훈(2021), Forecasting Bulk Freight Rates with Machine Learning Methods, 한국컴퓨터정보학회논문지, 제26집 7호, 127-132. https://doi.org/10.9708/JKSCI.2021.26.07.127
  7. 최기홍.김부권(2022), 해상운임지수와 상품가격 사이의 동적 연계성 분석, 한국항만경제학회지, 제38집 2호, 49-67.
  8. Alizadeh, A. H. and N. K. Nomikos(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. https://doi.org/10.1016/j.tre.2004.02.002
  9. Baker, S. R., N. Bloom, and S. J. Davis(2016), Measuring economic policy uncertainty, The quarterly journal of economics, 131(4), 1593-1636. https://doi.org/10.1093/qje/qjw024
  10. Beenstock, M(1985), A theory of ship prices, Maritime Policy and Management, 12(3), 215-225. https://doi.org/10.1080/03088838500000028
  11. BenSaida, A., H. Litimi and O. Abdallah(2018), Volatility spillover shifts in global financial markets, Economic Modelling, 73, 343-353. https://doi.org/10.1016/j.econmod.2018.04.011
  12. Charemza, W. and M. Gronicki(1981), An econometric model of world shipping and shipbuilding, Maritime Policy & Management, 8(1), 21-30. https://doi.org/10.1080/03088838100000019
  13. Chen, F., Y. Miao, K. Tian, X. Ding and T. Li(2017), Multifractal cross-correlations between crude oil and tanker freight rate, Physica A: Statistical Mechanics and its Applications, 474, 344-354. https://doi.org/10.1016/j.physa.2017.01.069
  14. Chen, J., F. Jin, G. Ouyang, J. Ouyang, and F. Wen(2019), Oil price shocks, economic policy uncertainty and industrial economic growth in China, PloS one, 14(5), e0215397.
  15. Choi, K. H. and S. M. Yoon(2020), Asymmetric dependence between oil prices and maritime freight rates: A time-varying copula approach, Sustainability, 12(24), 10687.
  16. Dai, L., H. Hu, Y. Tao and S. Lee(2020), The volatility transmission between crude oil market and tanker freight market, International Journal of Shipping and Transport Logistics, 12(6), 619-634. https://doi.org/10.1504/IJSTL.2020.111122
  17. Degiannakis, S., G. Filis and V. Arora(2017), Oil prices and stock markets, Washington, US: Energy Information Administration.
  18. Diebold, F. X. and Y. 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
  19. 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
  20. 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
  21. Figuerola-Ferretti, I., J. R. McCrorie, J. R. and I. Paraskevopoulos(2020), Mild explosivity in recent crude oil prices, Energy Economics, 87, 104387.
  22. Geman, H. and W. O. Smith(2012), Shipping markets and freight rates: an analysis of the Baltic Dry Index, The Journal of Alternative Investments, 15(1), 98-109. https://doi.org/10.3905/jai.2012.15.1.098
  23. Geng, J. B., F. R. Chen, Q. Ji and B. Y. Liu(2021), Network connectedness between natural gas markets, uncertainty and stock markets, Energy Economics, 95, 105001.
  24. Hamilton, J. D.(1989), A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica: Journal of the econometric society, 357-384.
  25. Hamilton, J. D.(1990), Analysis of time series subject to changes in regime, Journal of econometrics, 45(1-2), 39-70. https://doi.org/10.1016/0304-4076(90)90093-9
  26. Hasan, M. B., M. Mahi, T. Sarker and M. R. Amin(2021), Spillovers of the COVID-19 pandemic: Impact on global economic activity, the stock market, and the energy sector, Journal of Risk and Financial Management, 14(5), 200.
  27. Hou, C. and B. H. Nguyen(2018), Understanding the US natural gas market: A Markov switching VAR approach, Energy Economics, 75, 42-53. https://doi.org/10.1016/j.eneco.2018.08.004
  28. Khan, K., C. W. Su, R. Tao and M. Umar(2021), How do geopolitical risks affect oil prices and freight rates?, Ocean & Coastal Management, 215, 105955.
  29. Kilian, L.(2009), Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market, American Economic Review, 99(3), 1053-69.
  30. Krolzig, H. M.(1997), The markov-switching vector autoregressive model, In Markov-Switching vector autoregressions (pp. 6-28), Springer, Berlin, Heidelberg.
  31. Krolzig, H. M.(2000), Predicting Markov-switching vector autoregressive processes (pp. 1-30), Oxford: Nuffield College.
  32. 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
  33. Lun, Y. V., K. H. Lai and T. E. Cheng(2010), Shipping and logistics management (pp. 205-218), London: Springer.
  34. Maitra, D., S. Chandra and S. R. Dash(2020), Liner shipping industry and oil price volatility: Dynamic connectedness and portfolio diversification, Transportation Research Part E: Logistics and Transportation Review, 138, 101962.
  35. Narayan, P. K., S. Narayan and K. P. Prabheesh(2014), Stock returns, mutual fund flows and spillover shocks, Pacific-Basin Finance Journal, 29, 146-162. https://doi.org/10.1016/j.pacfin.2014.03.007
  36. Oomen, J.(2012), The Baltic Dry Index: A predictor of stock market returns, Unpublished Master Thesis, Tilburg, Tilburg University Department of Finance.
  37. Poulakidas, A. and F. Joutz(2009), Exploring the link between oil prices and tanker rates, Maritime Policy & Management, 36(3), 215-233. https://doi.org/10.1080/03088830902861094
  38. Ringim, S. H., A. Alhassan, H. Gungor and F. V. Bekun(2022), Economic Policy Uncertainty and Energy Prices: Empirical Evidence from Multivariate DCC-GARCH Models, Energies, 15(10), 3712.
  39. Scarcioffolo, A. R. and X. L. Etienne(2021), Regime-switching energy price volatility: The role of economic policy uncertainty, International Review of Economics & Finance, 76, 336-356. https://doi.org/10.1016/j.iref.2021.05.012
  40. Shi, W., Z. Yang and K. X. Li(2013), The impact of crude oil price on the tanker market, Maritime Policy & Management, 40(4), 309-322.
  41. Tao, R., C. W. Su, Y. Xiao, K. Dai and F. Khalid(2021), Robo advisors, algorithmic trading and investment management: wonders of fourth industrial revolution in financial markets, Technological Forecasting and Social Change, 163, 120421.
  42. Yang, L.(2019), Connectedness of economic policy uncertainty and oil price shocks in a time domain perspective, Energy Economics, 80, 219-233. https://doi.org/10.1016/j.eneco.2019.01.006