Skip to main content
SpringerLink
Log in

Shipping Economics Development: A Review from the Perspective of the Shipping Industry Chain for the Past Four Decades

  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

Abstract

To know the development of shipping economics, it is meaningful to overview shipping economics systemically from the perspective of markets and the shipping industry chain. To stimulate future research, this article presents an introduction to the evolution of research models including static models, dynamic models and networks theory, the characteristics of shipping markets including volatility, seasonal and market cycle, and a comprehensive review of the development of shipping economics in the past four decades. We review shipping economics in the following steps: single market’s research is generalized including the freight market, financial market including FFA market and investment market, shipbuilding market, and secondhand market; two markets’ correlation, information transmission, spillover effects, and other rules in shipping markets are surveyed; the correlation and risk of multi-markets are also investigated. Then, we summarize relationships of the shipping industry chain. Finally, we figure out issues in this field that need further study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. STOPFORD M. Maritime economics [M]. 3rd ed. London: Routledge, 2008.

    Book  Google Scholar 

  2. HEAVER T D. The many facets of maritime economics, in association [J]. Maritime Policy & Management, 1993, 20(2): 121–132.

    Article  Google Scholar 

  3. JANSSON J O, SHNEERSON D. Liner shipping economics [M]. London: Chapman and Hall, 1987.

    Book  Google Scholar 

  4. GOSS R O. An early history of maritime economics [J]. International Journal of Maritime Economics, 2002, 4(4): 390–404.

    Article  Google Scholar 

  5. WOO S H, BANG H S, MARTIN S, et al. Evolution of research themes in Maritime Policy & Management — 1973–2012 [J]. Maritime Policy & Management, 2013, 40(3): 200–225.

    Article  Google Scholar 

  6. ENGELEN S, MEERSMAN H, DE VOORDE E V. Using system dynamics in maritime economics: An endogenous decision model for shipowners in the dry bulk sector [J]. Maritime Policy & Management, 2006, 33(2): 141–158.

    Article  Google Scholar 

  7. VEENSTRA A W. The term structure of ocean freight rates [J]. Maritime Policy & Management, 1999, 26(3): 279–293.

    Article  Google Scholar 

  8. RANDERS J. Wave and oil tankers: Dynamics of the market for oil tankers to the year 2000 [C]//The 1981 System Dynamics Research Conference. New York: System Dynamics Society, 1981: Technical Session B1.

    Google Scholar 

  9. LYRIDIS D V, ZACHARIOUDAKIS P, MITROU P, et al. Forecasting tanker market using artificial neural networks [J]. Maritime Economics & Logistics, 2004, 6(2): 93–108.

    Article  Google Scholar 

  10. DINWOODIE J, MORRIS J. Tanker forward freight agreements: The future for freight futures? [J]. Maritime Policy & Management, 2003, 30(1): 45–58.

    Article  Google Scholar 

  11. CELIK M, ÇEBI S, KAHRAMAN C, et al. An integrated fuzzy QFD model proposal on routing of shipping investment decisions in crude oil tanker market [J]. Expert Systems with Applications, 2009, 36(3): 6227–6235.

    Article  Google Scholar 

  12. TVEDT J. Valuation of VLCCs under income uncertainty [J]. Maritime Policy & Management, 1997, 24(2): 159–174.

    Article  Google Scholar 

  13. POULAKIDAS A, JOUTZ F. Exploring the link between oil prices and tanker rates [J]. Maritime Policy & Management, 2009, 36(3): 215–233.

    Article  Google Scholar 

  14. CHEN F E, MIAO Y Q, TIAN K, et al. Multifractal cross-correlations between crude oil and tanker freight rate [J]. Physica A: Statistical Mechanics and Its Applications, 2017, 474: 344–354.

    Article  Google Scholar 

  15. XU J J, YIP T L, LIU L M. A directional relationship between freight and newbuilding markets: A panel analysis [J]. Maritime Economics & Logistics, 2011, 13(1): 44–60.

    Article  Google Scholar 

  16. ALIZADEH A H, NOMIKOS N K. Cost of carry, causality and arbitrage between oil futures and tanker freight markets [J]. Transportation Research Part E: Logistics and Transportation Review, 2004, 40(4): 297–316.

    Article  Google Scholar 

  17. ALEXANDRIDIS G, SAHOO S, VISVIKIS I. Economic information transmissions and liquidity between shipping markets: New evidence from freight derivatives [J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 98: 82–104.

    Article  Google Scholar 

  18. GLEN D R. The modelling of dry bulk and tanker markets: A survey [J]. Maritime Policy & Management, 2006, 33(5): 431–445.

    Article  Google Scholar 

  19. BEENSTOCK M, VERGOTTIS A. An econometric model of the world tanker market [J]. Journal of Transport Economics and Policy, 1989, 23(3): 263–280.

    Google Scholar 

  20. SIMS C A. Macroeconomics and reality [J]. Econometrica, 1980, 48(1): 1–48.

    Article  MathSciNet  Google Scholar 

  21. ENGLE R F, GRANGER C W J. Co-integration and error correction: Representation, estimation, and testing [J]. Econometrica, 1987, 55, 251–276.

    Article  MathSciNet  MATH  Google Scholar 

  22. SIMS C A, STOCK J H, WATSON M W. Inference in linear time series models with some unit roots [J]. Econometrica, 1990, 58(1): 113.

    Article  MathSciNet  MATH  Google Scholar 

  23. ENGLE R F. Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation [J]. Econometrica, 1982, 50(4): 987–1008.

    Article  MathSciNet  MATH  Google Scholar 

  24. BOLLERSLEV T. Generalized autoregressive conditional heteroskedasticity [J]. Journal of Econometrics, 1986, 31(3): 307–327.

    Article  MathSciNet  MATH  Google Scholar 

  25. GAVRIILIDIS K, KAMBOUROUDIS D S, TSAKOU K, et al. Volatility forecasting across tanker freight rates: The role of oil price shocks [J]. Transportation Research Part E: Logistics and Transportation Review, 2018, 118: 376–391.

    Article  Google Scholar 

  26. YANG Y Y, LIU C, SUN X L, et al. Spillover effect of international crude oil market on tanker market [J]. International Journal of Global Energy Issues, 2015, 38(4/5/6): 257.

    Article  Google Scholar 

  27. TORRENCE C, COMPO G P. A practical guide to wavelet analysis [J]. Bulletin of the American Meteorological Society, 1998, 79(1): 61–78.

    Article  Google Scholar 

  28. PEPPIN R J. An introduction to random vibrations, spectral and wavelet analysis [J]. Shock and Vibration, 1994, 1: 561605.

    Article  Google Scholar 

  29. YANG Z Z, JIN L J, WANG M H. Forecasting Baltic Panamax index with support vector machine [J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(3): 50–57.

    Article  Google Scholar 

  30. BENVENISTE A, NIKOUKHAH R, WILLSKY A S. Multiscale system theory [J]. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1994, 41(1): 2–15.

    Article  MathSciNet  MATH  Google Scholar 

  31. SUN X L, TANG L, YANG Y Y, et al. Identifying the dynamic relationship between tanker freight rates and oil prices: In the perspective of multiscale relevance [J]. Economic Modelling, 2014, 42: 287–295.

    Article  Google Scholar 

  32. LIU C, SUN X L, WANG J, et al. Multiscale information transmission between commodity markets: An EMD-based transfer entropy network [J]. Research in International Business and Finance, 2021, 55: 101318.

    Article  Google Scholar 

  33. MICHIS A A, NASON G P. Case study: Shipping trend estimation and prediction via multiscale variance stabilisation [J]. Journal of Applied Statistics, 2017, 44(15): 2672–2684.

    Article  MathSciNet  MATH  Google Scholar 

  34. YEGNANARAYANA B. Artificial neural networks[M]. New Delhi: PHI Learning Pvt Ltd, 2005

    Google Scholar 

  35. ZHANG X, XUE T Y, EUGENE STANLEY H. Comparison of econometric models and artificial neural networks algorithms for the prediction of Baltic dry index [J]. IEEE Access, 2019, 7: 1647–1657.

    Article  Google Scholar 

  36. AN Q E, WANG L, QU D B, et al. Dependency network of international oil trade before and after oil price drop [J]. Energy, 2018, 165: 1021–1033.

    Article  Google Scholar 

  37. SCHRAMM H J, MUNIM Z H. Container freight rate forecasting with improved accuracy by integrating soft facts from practitioners[J]. Research in Transportation Business and Management, 2021, 41: 100662.

    Article  Google Scholar 

  38. DURU O, YOSHIDA S. Judgmental forecasting in the dry bulk shipping business: Statistical vs. judgmental approach [J]. The Asian Journal of Shipping and Logistics, 2009, 25(2): 189–217.

    Article  Google Scholar 

  39. MUNIM Z H, SCHRAMM H J. Forecasting container shipping freight rates for the Far East-Northern Europe trade lane [J]. Maritime Economics & Logistics, 2017, 19(1): 106–125.

    Article  Google Scholar 

  40. HAN Q Q, YAN B, NING G B, et al. Forecasting dry bulk freight index with improved SVM [J]. Mathematical Problems in Engineering, 2014, 2014: 460684.

    Article  Google Scholar 

  41. WU C Y, CHOU H C, LIU C L. Fear index and freight rates in dry-bulk shipping markets [J]. Applied Economics, 2021, 53(11): 1235–1248.

    Article  Google Scholar 

  42. BATCHELOR R, ALIZADEH A, VISVIKIS I. Forecasting spot and forward prices in the international freight market [J]. International Journal of Forecasting, 2007, 23(1): 101–114.

    Article  Google Scholar 

  43. KAVUSSANOS M G, NOMIKOS N K. Price discovery, causality and forecasting in the freight futures market [J]. Review of Derivatives Research, 2003, 6(3): 203–230.

    Article  MATH  Google Scholar 

  44. KAVUSSANOS M G, VISVIKIS I D. Market interactions in returns and volatilities between spot and forward shipping freight markets [J]. Journal of Banking & Finance, 2004, 28(8): 2015–2049.

    Article  Google Scholar 

  45. ZHANG J, ZENG Q C. Modelling the volatility of the tanker freight market based on improved empirical mode decomposition [J]. Applied Economics, 2017, 49(17): 1655–1667.

    Article  Google Scholar 

  46. PROKOPCZUK M. Pricing and hedging in the freight futures market [J]. Journal of Futures Markets, 2011, 31(5): 440–464.

    Article  Google Scholar 

  47. NOMIKOS N K, KYRIAKOU I, PAPAPOSTOLOU N C, et al. Freight options: Price modelling and empirical analysis [J]. Transportation Research Part E: Logistics and Transportation Review, 2013, 51: 82–94.

    Article  Google Scholar 

  48. BENTH F E, KOEKEBAKKER S. Stochastic modeling of Supramax spot and forward freight rates [J]. Maritime Economics & Logistics, 2016, 18(4): 391–413.

    Google Scholar 

  49. KOEKEBAKKER S, OS ADLAND R. Modelling forward freight rate dynamics—empirical evidence from time charter rates[J]. Maritime Policy & Management, 2004, 31(4): 319–335.

    Article  Google Scholar 

  50. ALIZADEH A H, HUANG C Y, VAN DELLEN S. A regime switching approach for hedging tanker shipping freight rates [J]. Energy Economics, 2015, 49: 44–59.

    Article  Google Scholar 

  51. BYRKJELAND M N, ERIKSEN T M. Can you hedge dry bulk stock prices using forward freight agreements? A study of risk management in the dry bulk shipping stock market [D]. Bergen: Norwegian School of Economics, 2019.

    Google Scholar 

  52. BAIRD A J. The economics of container transhipment in Northern Europe [J]. International Journal of Maritime Economics, 2002, 4(3): 249–280.

    Article  Google Scholar 

  53. GREENWOOD R, HANSON S G. Waves in ship prices and investment [J]. The Quarterly Journal of Economics, 2015, 130(1): 55–109.

    Article  MATH  Google Scholar 

  54. BESSLER W, DROBETZ W, SEIDEL J. Ship funds as a new asset class: An empirical analysis of the relationship between spot and forward prices in freight markets [J]. Journal of Asset Management, 2008, 9(2): 102–120.

    Article  Google Scholar 

  55. GOULIELMOS A M, PSIFIA M. Shipping finance: Time to follow a new track? [J]. Maritime Policy & Management, 2006, 33(3): 301–320.

    Article  Google Scholar 

  56. ALIZADEH A H, NOMIKOS N K. Trading strategies in the market for tankers [J]. Maritime Policy & Management, 2006, 33(2): 119–140.

    Article  Google Scholar 

  57. KYRIAKOU I, POULIASIS P K, PAPAPOSTOLOU N C, et al. Income uncertainty and the decision to invest in bulk shipping [J]. European Financial Management, 2018, 24(3): 387–417.

    Article  Google Scholar 

  58. ROUSOS E P, LEE B S. Multicriteria analysis in shipping investment evaluation [J]. Maritime Policy & Management, 2012, 39(4): 423–442.

    Article  Google Scholar 

  59. POULIASIS P K, PAPAPOSTOLOU N C, KYRIAKOU I, et al. Shipping equity risk behavior and portfolio management [J]. Transportation Research Part A: Policy and Practice, 2018, 116: 178–200.

    Google Scholar 

  60. YIN J B, WU Y J, LU L J. Assessment of investment decision in the dry bulk shipping market based on real options thinking and the shipping cycle perspective [J]. Maritime Policy & Management, 2019, 46(3): 330–343.

    Article  Google Scholar 

  61. SYRIOPOULOS T, BAKOS G. Investor herding behaviour in globally listed shipping stocks [J]. Maritime Policy & Management, 2019, 46(5): 545–564.

    Article  Google Scholar 

  62. PAPAPOSTOLOU N C, NOMIKOS N K, POULIASIS P K, et al. Investor sentiment for real assets: The case of dry bulk shipping market [J]. Review of Finance, 2014, 18(4): 1507–1539.

    Article  Google Scholar 

  63. HALE C, VANAGS A. The market for second-hand ships: Some results on efficiency using cointegration [J]. Maritime Policy & Management, 1992, 19(1): 31–39.

    Article  Google Scholar 

  64. TSOLAKIS S D, CRIDLAND C, HARALAMBIDES H E. Econometric modelling of second-hand ship prices [J]. Maritime Economics & Logistics, 2003, 5(4): 347–377.

    Article  Google Scholar 

  65. ADLAND R, JIA H Y. Shipping market integration: The case of sticky newbuilding prices [J]. Maritime Economics & Logistics, 2015, 17(4): 389–398.

    Article  Google Scholar 

  66. RAJU T B, SENGAR V S, JAYARAJ R, et al. Study of volatility of new ship building prices in LNG shipping [J]. International Journal of e-Navigation and Maritime Economy, 2016, 5: 61–73.

    Article  Google Scholar 

  67. SYRIOPOULOS T, ROUMPIS E. Price and volume dynamics in second-hand dry bulk and tanker shipping markets [J]. Maritime Policy & Management, 2006, 33(5): 497–518.

    Article  Google Scholar 

  68. ADLAND R, KOEKEBAKKER S. Ship valuation using cross-sectional sales data: A multivariate non-parametric approach [J]. Maritime Economics & Logistics, 2007, 9(2): 105–118.

    Article  Google Scholar 

  69. EVANS J J. An analysis of efficiency of the bulk shipping markets [J]. Maritime Policy & Management, 1994, 21(4): 311–329.

    Article  Google Scholar 

  70. ANGELIDIS T, SKIADOPOULOS G. Measuring the market risk of freight rates: A value-at-risk approach [J]. International Journal of Theoretical and Applied Finance, 2008, 11(5): 447–469.

    Article  Google Scholar 

  71. KALOUPTSIDI M. Time to build and fluctuations in bulk shipping [J]. American Economic Review, 2014, 104(2): 564–608.

    Article  Google Scholar 

  72. WANG J H, LU J, WEI F, et al. Risk evaluation on extreme volatilities of dry bulk ocean freight market [C]//International Forum on Shipping, Ports and Airports (IFSPA 2008)-Trade-Based Global Supply Chain and Transport Logistics Hubs: Trends and Future Development. Hong Kong: IFSPA, 2008: 517–537.

    Google Scholar 

  73. DURU O. A multivariate model of fuzzy integrated logical forecasting method (M-FILF) and multiplicative time series clustering: A model of time-varying volatility for dry cargo freight market [J]. Expert Systems with Applications, 2012, 39(4): 4135–4142.

    Article  Google Scholar 

  74. ZENG Q C, QU C R. An approach for Baltic Dry Index analysis based on empirical mode decomposition [J]. Maritime Policy & Management, 2014, 41(3): 224–240.

    Article  Google Scholar 

  75. DAI S Y, ZENG Y D, CHEN F E. The scaling behavior of bulk freight rate volatility [J]. International Journal of Transport Economics, 2016, 43(1/2): 85–104.

    Google Scholar 

  76. CHEN Y S, WANG S T. The empirical evidence of the leverage effect on volatility in international bulk shipping market [J]. Maritime Policy & Management, 2004, 31(2): 109–124.

    Article  Google Scholar 

  77. LUO M F, FAN L X, LIU L M. An econometric analysis for container shipping market [J]. Maritime Policy & Management, 2009, 36(6): 507–523.

    Article  Google Scholar 

  78. THEODOSSIOU P, TSOUKNIDIS D, SAVVA C. Freight rates in downside and upside markets: Pricing of own and spillover risks from other shipping segments [J]. Journal of the Royal Statistical Society: Series A (Statistics in Society), 2020, 183(3): 1097–1119.

    Article  MathSciNet  Google Scholar 

  79. JING L, MARLOW P B, HUI W. An analysis of freight rate volatility in dry bulk shipping markets [J]. Maritime Policy & Management, 2008, 35(3): 237–251.

    Article  Google Scholar 

  80. DIKOS G, MARCUS H S, PAPADATOS M P, et al. Niver lines: A system-dynamics approach to tanker freight modeling [J]. Interfaces, 2006, 36(4): 326–341

    Article  Google Scholar 

  81. KAVUSSANOS M G, ALIZADEH-M A H. Seasonality patterns in dry bulk shipping spot and time charter freight rates [J]. Transportation Research Part E: Logistics and Transportation Review, 2001, 37(6): 443–467.

    Article  Google Scholar 

  82. KAVUSSANOS M G, ALIZADEH-M A H. Seasonality patterns in tanker spot freight rate markets [J]. Economic Modelling, 2002, 19(5): 747–782.

    Article  Google Scholar 

  83. TVEDT J. A new perspective on price dynamics of the dry bulk market [J]. Maritime Policy & Management, Taylor & Francis Journals, 2003, 30(3): 221–230.

    Article  Google Scholar 

  84. ADLAND R, JIA H Y, LU J. Price dynamics in the market for Liquid Petroleum Gas transport [J]. Energy Economics, 2008, 30(3): 818–828.

    Article  Google Scholar 

  85. KO B W. A mixed-regime model for dry bulk freight market [J]. The Asian Journal of Shipping and Logistics, 2010, 26(2): 185–204.

    Article  Google Scholar 

  86. ABOUARGHOUB W, MARISCAL I, MARISCAL F, et al. Dynamic earnings within tanker markets: An investigation of exogenous and endogenous structure breaks [J]. American International Journal of Contemporary Research, 2012, 2(1): 132–147.

    Google Scholar 

  87. MERIKAS A G, MERIKAS A A, POLEMIS D, et al. The economics of concentration in shipping: Consequences for the VLCC tanker sector [J]. Maritime Economics & Logistics, 2014, 16(1): 92–110.

    Article  Google Scholar 

  88. DAI L, HU H, CHEN F E, et al. The dynamics between newbuilding ship price volatility and freight volatility in dry bulk shipping market [J]. International Journal of Shipping and Transport Logistics, 2015, 7(4): 393.

    Article  Google Scholar 

  89. POBLACION J. The stochastic seasonal behavior of freight rate dynamics [J]. Maritime Economics & Logistics, 2015, 17(2): 142–162.

    Article  Google Scholar 

  90. FAN L, ZHANG S, YIN J. Structural analysis of shipping fleet capacity [J]. Journal of Advanced Transportation, 2018, 2018: 3854090.

    Article  Google Scholar 

  91. REGLI F, NOMIKOS N K. The eye in the sky: Freight rate effects of tanker supply [J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 125: 402–424.

    Article  Google Scholar 

  92. HAYASHI K. Stationarity of spot freight rates considering supply/demand effect [J]. Journal of Shipping and Trade, 2020, 5(1): 1–9.

    Article  Google Scholar 

  93. YIN J B, SHI J H. Seasonality patterns in the container shipping freight rate market [J]. Maritime Policy & Management, 2018, 45(2): 159–173.

    Article  Google Scholar 

  94. SCARSI R. The bulk shipping business: Market cycles and shipowners’ biases [J]. Maritime Policy & Management, 2007, 34(6): 577–590.

    Article  Google Scholar 

  95. RANDERS J, GÖLUKE U. Forecasting turning points in shipping freight rates: Lessons from 30 years of practical effort [J]. System Dynamics Review, 2007, 23(2/3): 253–284.

    Article  Google Scholar 

  96. JEON J W, DURU O, YEO G T. Modelling cyclic container freight index using system dynamics [J]. Maritime Policy & Management, 2020, 47(3): 287–303.

    Article  Google Scholar 

  97. CHISTÈ C, VAN VUUREN G. Investigating the cyclical behaviour of the dry bulk shipping market [J]. Maritime Policy & Management, 2014, 41(1): 1–19.

    Article  Google Scholar 

  98. CHEN J H, XUE K, SONG L, et al. Periodicity of world crude oil maritime transportation: Case analysis of Aframax Tanker market [J]. Energy Strategy Reviews, 2019, 25: 47–55.

    Article  Google Scholar 

  99. BERG-ANDREASSEN J A. The relationship between period and spot rates in international maritime markets [J]. Maritime Policy & Management, 1997, 24(4): 335–350.

    Article  Google Scholar 

  100. GLEN D, OWEN M, VAN DER MEER R. Spot and time charter rates for tankers, 1970–77 [J]. Journal of Transport Economics and Policy, 1981, 15(1): 45–58.

    Google Scholar 

  101. BEENSTOCK M, VERGOTTIS A. The interdependence between the dry cargo and tanker markets [J]. Logistics and Transportation Review, 1993, 29(1): 3–38.

    Google Scholar 

  102. GLEN D R. The market for second-hand ships: Further results on efficiency using cointegration analysis [J]. Maritime Policy & Management, 1997, 24(3): 245–260.

    Article  MathSciNet  Google Scholar 

  103. HSIAO Y J, CHOU H C, WU C C. Return lead-lag and volatility transmission in shipping freight markets [J]. Maritime Policy & Management, 2014, 41(7): 697–714.

    Article  Google Scholar 

  104. XU J J, YIP T L, LIU L. Dynamic interrelationships between sea freight and shipbuilding markets [C]//International Forum on Shipping, Ports and Airports (IFSPA 2008)-Trade-Based Global Supply Chain and Transport Logistics Hubs: Trends and Future Development. Hong Kong: IFSPA, 2008: 480–494.

    Google Scholar 

  105. DAI L, HU H, ZHANG D. An empirical analysis of freight rate and vessel price volatility transmission in global dry bulk shipping market [J]. Journal of Traffic and Transportation Engineering (English Edition), 2015, 2(5): 353–361.

    Article  Google Scholar 

  106. RONEN D. The effect of oil price on containership speed and fleet size [J]. Journal of the Operational Research Society, 2011, 62(1): 211–216.

    Article  Google Scholar 

  107. RONEN D. The effect of oil price on the optimal speed of ships [J]. Journal of the Operational Research Society, 1982, 33(11): 1035–1040.

    Article  Google Scholar 

  108. SHI W M, YANG Z Z, LI K X. The impact of crude oil price on the tanker market [J]. Maritime Policy & Management, 2013, 40(4): 309–322.

    Article  Google Scholar 

  109. ZHANG Y. Investigating dependencies among oil price and tanker market variables by copula-based multivariate models [J]. Energy, 2018, 161: 435–446.

    Article  Google Scholar 

  110. KAVUSSANOS M, VISVIKIS I, DIMITRAKOPOULOS D. Information linkages between Panamax freight derivatives and commodity derivatives markets [J]. Maritime Economics & Logistics, 2010, 12(1): 91–110.

    Article  Google Scholar 

  111. KAVUSSANOS M G, VISVIKIS I D, DIMITRAKOPOULOS D N. Economic spillovers between related derivatives markets: The case of commodity and freight markets [J]. Transportation Research Part E: Logistics and Transportation Review, 2014, 68: 79–102.

    Article  Google Scholar 

  112. AÇIK A, BAŞER S Ö. Impact of commodity price on freight market considering the 2008 crisis: An investigation of iron ore price and capesize shipping rates [J]. Journal of Maritime Transport and Logistics, 2021, 2(2): 62–71.

    Google Scholar 

  113. AÇIK A, BASER S Ö. Asymmetric causality from commodity prices to shipping markets: An empirical research on ISTFIX region [J]. World Review of Intermodal Transportation Research, 2020, 9(1): 47.

    Article  Google Scholar 

  114. MELAS K D, MICHAIL N A. The relationship between commodity prices and freight rates in the dry bulk shipping segment: A threshold regression approach [J]. Maritime Transport Research, 2021, 2: 100025.

    Article  Google Scholar 

  115. MICHAIL N A, MELAS K D. Market interactions between agricultural commodities and the dry bulk shipping market [J]. The Asian Journal of Shipping and Logistics, 2021, 37(1): 73–81.

    Article  Google Scholar 

  116. ALIZADEH A H, MURADOGLU G. Stock market efficiency and international shipping-market information [J]. Journal of International Financial Markets, Institutions and Money, 2014, 33: 445–461.

    Article  Google Scholar 

  117. GONG Y T, LI K X, CHEN S L, et al. Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war [J]. Transportation Research Part E: Logistics and Transportation Review, 2020, 136: 101900.

    Article  Google Scholar 

  118. DROBETZ W, GAVRIILIDIS K, KROKIDA S I, et al. The effects of geopolitical risk and economic policy uncertainty on dry bulk shipping freight rates [J]. Applied Economics, 2021, 53(19): 2218–2229.

    Article  Google Scholar 

  119. ADLAND R, JIA H Y, STRANDENES S. Asset bubbles in shipping? An analysis of recent history in the drybulk market [J]. Maritime Economics & Logistics, 2006, 8(3): 223–233.

    Article  Google Scholar 

  120. LUN Y H V, QUADDUS M A. An empirical model of the bulk shipping market [J]. International Journal of Shipping and Transport Logistics, 2009, 1(1): 37–54.

    Article  Google Scholar 

  121. MICHAIL N A, MELAS K D. Quantifying the relationship between seaborne trade and shipping freight rates: A Bayesian vector autoregressive approach [J]. Maritime Transport Research, 2020, 1: 100001.

    Article  Google Scholar 

  122. SUN X L, HARALAMBIDES H, LIU H L. Dynamic spillover effects among derivative markets in tanker shipping [J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 122: 384–409.

    Article  Google Scholar 

  123. MARSHALL A. Principles of economics: Unabridged eighth edition [M]. New York: Cosimo, Inc, 2009.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Qiliang Xia  (夏启亮) & Feier Chen  (陈飞儿)

  2. Shanghai Jiao Tong University, Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai, 200240, China

    Qiliang Xia  (夏启亮) & Feier Chen  (陈飞儿)

Authors
  1. Qiliang Xia  (夏启亮)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feier Chen  (陈飞儿)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feier Chen  (陈飞儿).

Additional information

Foundation item: the Natural Science Foundation of Shanghai (No. 18ZR1420200)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, Q., Chen, F. Shipping Economics Development: A Review from the Perspective of the Shipping Industry Chain for the Past Four Decades. J. Shanghai Jiaotong Univ. (Sci.) 27, 424–436 (2022). https://doi.org/10.1007/s12204-022-2449-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12204-022-2449-y

Key words

CLC number

Document code

Search

Navigation