DOI QR코드

DOI QR Code

Quantile Co-integration Application for Maritime Business Fluctuation

분위수 공적분 모형과 해운 경기변동 분석

  • Received : 2022.06.10
  • Accepted : 2022.06.29
  • Published : 2022.06.30

Abstract

In this study, we estimate the quantile-regression framework of the shipping industry for the Capesize used ship, which is a typical raw material transportation from January 2000 to December 2021. This research aims two main contributions. First, we analyze the relationship between the Capesize used ship, which is a typical type in the raw material transportation market, and the freight market, for which mixed empirical analysis results are presented. Second, we present an empirical analysis model that considers the structural transformation proposed in the Hyunsok Kim and Myung-hee Chang(2020a) study in quantile-regression. In structural change investigations, the empirical results confirm that the quantile model is able to overcome the problems caused by non-stationarity in time series analysis. Then, the long-run relationship of the co-integration framework divided into long and short-run effects of exogenous variables, and this is extended to a prediction model subdivided by quantile. The results are the basis for extending the analysis based on the shipping theory to artificial intelligence and machine learning approaches.

본 연구는 2000년 1월부터 2021년 12월까지의 대표적 원자재 운송 수단인 Capesize 중고선가를 대상으로 해운산업에 대한 분위수 모형을 추정한다. 본 연구는 두 가지 학술적 기여를 목표로 한다. 첫째, 혼재된 실증분석 결과가 제기되는 원자재 운송 시장의 대표적 선종인 Capesize 중고선과 운임시장의 연관성을 분석한다. 둘째, 분위수 회귀로 김현석·장명희(2020a) 연구에서 제기하는 구조변환을 고려하는 실증분석 모형을 제시한다. 분석 결과는 분위수 모형은 시계열 자료에서 구조변화를 분석에 반영함으로써 오차의 불안정성으로 제기되는 문제를 우회할 수 있음을 확인한다. 그리고 공적분 모형의 장기 균형관계를 장기와 단기 추정변수를 통해 외생변수의 장·단기 영향으로 구분하고, 이를 분위별로 세분화한 예측으로 확장한다. 이상의 추정결과는 해운 이론모형에 기반한 분석을 인공지능과 기계학습으로 확장할 수 있는 근거가 된다.

Keywords

Acknowledgement

이 과제는 부산대학교 교수국외장기파견지원비에 의하여 연구되었음

References

  1. 김원재(2011), 해운산업 수익성 제고 투자의사결정 모델구축에 관한 연구, 한국항만경제학회지, 제27집 제2호, 297-311.
  2. 김현석.장명희(2013), 벙커가격과 건화물선 지수(Baltic Dry-bulk Index) 간의 비대칭 장기균형 분석, 한국항만경제학회지, 제29권 제2호, 63-79.
  3. 김현석.장명희(2013), 물동량과 산업생산지수 간의 비선형공적분 검정, 해운물류연구, 제29권 제4호, 1079-1093 https://doi.org/10.37059/TJOSAL.2013.29.4.1079
  4. 김현석.장명희(2014a), 해운경기변동과 선박수요.공급 간의 비선형 장기균형관계 분석, 한국해운물류, 제30권 제2호, 381-399.
  5. 김현석.장명희(2014b), Bayesian VAR를 이용한 해운경기, 환율 그리고 산업생산 간의 동태적 상관분석, 한국항만경제학회지, 제30권 제2호, 77-92.
  6. 김현석.장명희(2014c), 운임수익과 선박가격 변동이 선박투자결정에 미치는 영향 -비선형 장기균형관계, 한국해운물류, 제30권 제4호, 859-877.
  7. 김현석.장명희(2020a), 고빈도 장기시계열을 활용한 해운산업의 경기변동 구조변화 분석, 한국해운물류, 제36권 제2호, 285-304.
  8. 김현석.장명희(2020b), 해운경기변동과 선박시장에 대한 다차원 혼합 패널 인과성 분석, 한국항만경제학회지, 제36권 제2호, 109-123.
  9. Adland, R., Jia, H. and S. Strandenes (2006), Asset Bubbles in Shipping? An Analysis of Recent History in the Drybulk Market, Maritime Economics & Logistics, 8(3), 223-233. https://doi.org/10.1057/palgrave.mel.9100162
  10. Alizadeh, A. and Nomikos, N. K. (2007), Investment timing and trading strategies in the sale and purchase market for ships, Transportation Research Part B: Methodological, 41(1), 126-143. https://doi.org/10.1016/j.trb.2006.04.002
  11. Beenstock, M. (1985), A Theory of Ship Prices, Maritime Policy and Management, 12, 215-225. https://doi.org/10.1080/03088838500000028
  12. Beenstock, M., Vergottis, A.(1989), An econometric model of the world market for dry cargo freight and shipping. Applied Economics 21(3), 339-356. https://doi.org/10.1080/758522551
  13. Chiste, C., and G. V. Vuuren.(2014). Investigating the Cyclical Behavior of the Dry Bulk Shipping Market. Maritime Policy & Management 41 (1): 1-19. doi:10.1080/03088839.2013.780216.
  14. Cho, J. S., M. J. Greenwood-Nimmo, and Y. Shin (2019). "Estimating the Nonlinear Autoregressive Distributed Lag Model for Time Series Data with Drifts," Mimeo: University of York.
  15. Cho, J. S., T.-H. Kim, and Y. Shin(2015). "Quantile Cointegration in the Autoregressive Distributed Lag Modeling Framework," Journal of Econometrics, 188, 281-300. https://doi.org/10.1016/j.jeconom.2015.05.003
  16. Dikos, G., & Marcus, H. (2003). The term structure of second-hand prices: A structural partial equilibrium model. Maritime Economics & Logistics, 5(3), 251-267. https://doi.org/10.1057/palgrave.mel.9100084
  17. Duru, O. (2013). Irrational exuberance, overconfidence and short-termism: Knowledge-to-action asymmetry in shipping asset management. The Asian Journal of Shipping and Logistics, 29(1), 43-58. https://doi.org/10.1016/j.ajsl.2013.05.003
  18. Duru, O. (2018). Shipping business unwrapped: Illusion, Bias and fallacy in the shipping business. Routledge.
  19. Engle, R. F. and C. W. J. Granger (1987) "Co-Integration and Error-Correction: Representation, Estimation, and Testing." Econometrica. 55, 251-276. https://doi.org/10.2307/1913236
  20. Forrester, J. W. (1958). Industrial dynamics. A major breakthrough for decision mak- ers. Harvard business review, 36(4), 37-66.
  21. Girgin, S. C., Karlis, T., & Nguyen, H.-O. (2018). A critical review of the literature on firm-level theories on ship investment. International Journal of Financial Studies, 6(11).
  22. Gkochari, C.(2015), Optimal investment timing in the dry bulk shipping sector, Transportation Research Part E: Logistics and Transportation Review 79, 102-109. https://doi.org/10.1016/j.tre.2015.02.018
  23. Granger, C. W. J. (1969). "Investigating Causal Relations by Econometric Models and Cross-spectral Methods". Econometrica. 37 (3): 424-438. https://doi.org/10.2307/1912791
  24. Greenwood, R., & Hanson, S. G. (2015). Waves in ship prices and investment. The Quarterly Journal of Economics, 130(1), 55-109. https://doi.org/10.1093/qje/qju035
  25. Haralambides, H. E., Tsolakis, S. D., Cridland, C., (2004). Econometric Modelling Of Newbuilding And Secondhand Ship Prices. Research in Transportation Economics 12, 65-105. https://doi.org/10.1016/S0739-8859(04)12003-9
  26. Hawdon, D.(1978). Tanker freight rates in the short and long run. Applied Economics 10(3), 203-218. https://doi.org/10.1080/758527274
  27. Jeon, J.-W., Duru, O., & Yeo, G.-T. (2020). Modelling cyclic container freight index using system dynamics. Maritime Policy & Management, 47(3), 287-303. https://doi.org/10.1080/03088839.2019.1708984
  28. Kalouptsidi, M.,(2014). Time to build and fluctuations in shipping. American Economic Review 104(2), 564-608. https://doi.org/10.1257/aer.104.2.564
  29. Kavussanos, M. G.(1997). The dynamics of time-varying volatilities in different size second-hand ship prices of the dry-cargo sector. Applied Economics 29 (4), 433-443. https://doi.org/10.1080/000368497326930
  30. Kavussanos, M. G., and A. H. Alizadeh-M.(2001). Seasonality Patterns in Dry Back Shipping Spot and Time Charter Freight Rates. Transportation Research Part E 37 (6): 443-467. https://doi.org/10.1016/S1366-5545(01)00004-7
  31. Kavussanos, M.G., Alizadeh, A.H.(2002). Efficient pricing of ships in the dry bulk sector of the shipping industry. Maritime Policy & Management 29(3), 303-330. https://doi.org/10.1080/03088830210132588
  32. Koekebakker, S., Adland, R.,(2004). Market Efficiency in the Second-hand Market for Bulk Ships.Maritime Economics and Logistics 6(1), 1-15. https://doi.org/10.1057/palgrave.mel.9100092
  33. Koenker, R. and G. Bassett(1978). "Regression Quantiles," Econometrica, 46, 33-50. https://doi.org/10.2307/1913643
  34. Kou, Y., Liu, L., Luo, M.,(2014). Lead-lag relationship between new-building and second-hand ship prices. Maritime Policy & Management 41 (4), 303-327. https://doi.org/10.1080/03088839.2013.821209
  35. Levin, A., C.-F. Lin, and C.-S. J. Chu.(2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics 108: 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7
  36. Lin, F., and N. C. S. Sim. 2013. Trade, Income and the Baltic Dry Index. European Economic Review 59 (4): 1-18. doi: 10.1016/j.euroecorev.2012.12.004.
  37. Li, J., & Parsons, M. G. (1997). Forecasting tanker freight rate using neural networks. Maritime Policy & Management, 24(1), 9-30. https://doi.org/10.1080/03088839700000053
  38. Lyridis, D., Zacharioudakis, P., Mitrou, P., & Mylonas, A. (2004). Forecasting tanker market using arti ficial neural networks. Maritime Economics & Logistics, 6(2), 93-108. https://doi.org/10.1057/palgrave.mel.9100097
  39. Merikas, A. G., Merika, A. A., & Koutroubousis, G. (2008). Modelling the invest- ment decision of the entrepreneur in the tanker sector: Choosing between a second-hand vessel and a newly built one. Maritime Policy & Management, 35(5), 433-447. https://doi.org/10.1080/03088830802352053
  40. Pesaran, M. H. AND Y. Shin (1998). "An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis," in Econometrics and Economic Theory: The Ragnar Frisch Centennial Symposium, ed. by S. Strom, Cambridge: Cambridge University Press, Econometric Society Monographs, 371-413.
  41. Pesaran, M. H., Y. Shin, AND R. J. Smith (2001). "Bounds Testing Approaches to the Analysis of Level Relationships," Journal of Applied Econometrics, 16, 289-326. https://doi.org/10.1002/jae.616
  42. Rau, P., Spinler, S.(2016). Investment into container shipping capacity: A real options approach in oligopolistic competition. Transportation Research Part E: Logistics and Transportation Review 93, 130-147. https://doi.org/10.1016/j.tre.2016.05.012
  43. Sodal, S., Koekebakker, S., Adland, R.,(2009). Trading rules with analytical ship valuation under stochastic freight rates. Applied Economics 41(22), 2793-2807. https://doi.org/10.1080/00036840701720853
  44. Stopford, M.,(2009). Maritime Economics, 3rd ed. Routledge, London.
  45. Strandenes, S. P.(1984). Price determination in the time charter and second hand markets. Center for Applied Research, Norwegian School of Economics and Business Administration, Working Paper MU, 6.
  46. Thalassinos, I., Hanias, M. P., Curtis, G., & Thalassinos, E. (2013). Forecasting financial indices: The Baltic dry indices. In Marine navigation and safety of sea transportation: STCW, maritime education and training (MET), human resources and crew manning, maritime policy, logistics and economic matters. pp. 190-283.
  47. Toda, H.Y., Yamamoto, T.(1995), Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics 66, 225-250. https://doi.org/10.1016/0304-4076(94)01616-8
  48. Tsolakis, S., Cridland, C., & Haralambides, H. (2003). Econometric modelling of second-hand ship prices. Maritime Economics & Logistics, 5(4), 347-377. https:// doi.org/10.1057/palgrave.mel.9100086
  49. Tsouknidis, D. A.(2016). Dynamic Volatility Spillovers across Shipping Freight Markets. Transportation Research Part E 91: 90-111. https://doi.org/10.1016/j.tre.2016.04.001
  50. Tvedt, J.(2003). A New Perspective on Price Dynamics of the Dry Bulk Market. Maritime Policy & Management 30 (3): 221-230. https://doi.org/10.1080/0308883032000133413
  51. Uyar, K., & Ilhan, A. (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
  52. Zeng, Q., Qu, C., Ng, A. K., & Zhao, X. (2016). A new approach for Baltic Dry Index fore- casting based on empirical mode decomposition and neural networks. Maritime Economics & Logistics, 18(2), 192-210. https://doi.org/10.1057/mel.2015.2