IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v78y2019icp412-427.html
   My bibliography  Save this article

A copula-GARCH approach for analyzing dynamic conditional dependency structure between liquefied petroleum gas freight rate, product price arbitrage and crude oil price

Author

Listed:
  • Bai, Xiwen
  • Lam, Jasmine Siu Lee

Abstract

Global liquefied petroleum gas (LPG) trade shows an increasing trend but is under-researched. This paper focuses on the LPG market and aims to model the dependence dynamics among LPG freight rates, crude oil price and propane location arbitrage. Conditional copula-GARCH model is applied to estimate dependencies. Different types of copulas with both time-invariant and time-varying dependence structures are fitted and their suitability has been compared. The findings suggest that firstly, Baltic LPG (BLPG) freight rate and the arbitrage between propane Far East and Middle East prices have conditional time-varying dependence and the dependence is higher in market downturns. Furthermore, BLPG and the arbitrage between Far East and US propane prices, have symmetric dependence and such a relationship has strengthened since 2013. Secondly, Middle East propane price is found to have the strongest correlation with crude oil prices compared to Far East and US propane prices, indicating higher sensitivity to crude oil price changes. Last but not least, the relationship between crude oil and BLPG is relatively weak and mostly positive.

Suggested Citation

  • Bai, Xiwen & Lam, Jasmine Siu Lee, 2019. "A copula-GARCH approach for analyzing dynamic conditional dependency structure between liquefied petroleum gas freight rate, product price arbitrage and crude oil price," Energy Economics, Elsevier, vol. 78(C), pages 412-427.
    • Handle: RePEc:eee:eneeco:v:78:y:2019:i:c:p:412-427
    DOI: 10.1016/j.eneco.2018.10.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988318304341
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2018.10.032?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    2. Joe, H., 1993. "Parametric Families of Multivariate Distributions with Given Margins," Journal of Multivariate Analysis, Elsevier, vol. 46(2), pages 262-282, August.
    3. Wenming Shi & Zhongzhi Yang & Kevin X. Li, 2013. "The impact of crude oil price on the tanker market," Maritime Policy & Management, Taylor & Francis Journals, vol. 40(4), pages 309-322, July.
    4. Francis X. Diebold & Lee E. Ohanian & Jeremy Berkowitz, 1998. "Dynamic Equilibrium Economies: A Framework for Comparing Models and Data," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 433-451.
    5. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
    6. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    7. Tun‐Hsiang (Edward) Yu & David A. Bessler & Stephen W. Fuller, 2007. "Price Dynamics in U.S. Grain and Freight Markets," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 55(3), pages 381-397, September.
    8. Wu, Chih-Chiang & Chung, Huimin & Chang, Yu-Hsien, 2012. "The economic value of co-movement between oil price and exchange rate using copula-based GARCH models," Energy Economics, Elsevier, vol. 34(1), pages 270-282.
    9. Amir Alizadeh & Nikos Nomikos, 2004. "A Markov regime switching approach for hedging stock indices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(7), pages 649-674, July.
    10. Liu, Yan & Luger, Richard, 2009. "Efficient estimation of copula-GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2284-2297, April.
    11. Asche, Frank & Gjolberg, Ole & Volker, Teresa, 2003. "Price relationships in the petroleum market: an analysis of crude oil and refined product prices," Energy Economics, Elsevier, vol. 25(3), pages 289-301, May.
    12. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    13. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    14. Angela Poulakidas & Fred Joutz, 2009. "Exploring the link between oil prices and tanker rates," Maritime Policy & Management, Taylor & Francis Journals, vol. 36(3), pages 215-233, June.
    15. Rodriguez, Juan Carlos, 2007. "Measuring financial contagion: A Copula approach," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 401-423, June.
    16. Sun, Xiaolei & Tang, Ling & Yang, Yuying & Wu, Dengsheng & Li, Jianping, 2014. "Identifying the dynamic relationship between tanker freight rates and oil prices: In the perspective of multiscale relevance," Economic Modelling, Elsevier, vol. 42(C), pages 287-295.
    17. David Hummels, 2007. "Transportation Costs and International Trade in the Second Era of Globalization," Journal of Economic Perspectives, American Economic Association, vol. 21(3), pages 131-154, Summer.
    18. Skadberg, Kristopher & Wilson, William W. & Larsen, Ryan & Dahl, Bruce, 2015. "Spatial Competition, Arbitrage, and Risk in U.S. Soybeans," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(3), pages 1-15, September.
    19. Aloui, Riadh & Ben Aïssa, Mohamed Safouane & Nguyen, Duc Khuong, 2013. "Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 719-738.
    20. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    21. Atle Oglend, Morten E. Lindbäck, and Petter Osmundsen, 2015. "Shale Gas Boom Affecting the Relationship Between LPG and Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    22. Alexandros M. Goulielmos & Mariniki Psifia, 2007. "A study of trip and time charter freight rate indices: 1968--2003," Maritime Policy & Management, Taylor & Francis Journals, vol. 34(1), pages 55-67, February.
    23. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    24. Haigh, Michael S. & Bryant, Henry L., 2001. "The effect of barge and ocean freight price volatility in international grain markets," Agricultural Economics, Blackwell, vol. 25(1), pages 41-58, June.
    25. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    26. Osvaldo C. Silva Filho & Flavio A. Ziegelmann & Michael J. Dueker, 2014. "Assessing dependence between financial market indexes using conditional time-varying copulas: applications to Value at Risk (VaR)," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2155-2170, December.
    27. Joe, Harry & Hu, Taizhong, 1996. "Multivariate Distributions from Mixtures of Max-Infinitely Divisible Distributions," Journal of Multivariate Analysis, Elsevier, vol. 57(2), pages 240-265, May.
    28. Ling Hu, 2006. "Dependence patterns across financial markets: a mixed copula approach," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 717-729.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bai, Xiwen & Lam, Jasmine Siu Lee, 2021. "Freight rate co-movement and risk spillovers in the product tanker shipping market: A copula analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    2. Ngo Thai Hung, 2020. "Identifying the Dynamic Connectedness between Propane and Oil Prices: Evidence from Wavelet Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 315-326.
    3. Shirazi, Masoud, 2022. "Assessing energy trilemma-related policies: The world's large energy user evidence," Energy Policy, Elsevier, vol. 167(C).
    4. Yang, Dong & Zhang, Lingge & Luo, Meifeng & Li, Feng, 2020. "Does shipping market affect international iron ore trade?– An equilibrium analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    5. Lin, Saiyan & Chen, Rongda & Lv, Zhihong & Zhou, Tianqing & Jin, Chenglu, 2019. "Integrated measurement of liquidity risk and market risk of company bonds based on the optimal Copula model," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    6. Liu, Jianing & Man, Yuanyuan & Dong, Xiuliang, 2023. "Tail dependence and risk spillover effects between China's carbon market and energy markets," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 553-567.
    7. Bai, Xiwen & Lam, Jasmine Siu Lee, 2019. "A destination choice model for very large gas carriers (VLGC) loading from the US Gulf," Energy, Elsevier, vol. 174(C), pages 1267-1275.
    8. Lijuan Huo & Jin Seo Cho, 2021. "Testing for the sandwich-form covariance matrix of the quasi-maximum likelihood estimator," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 293-317, June.
    9. Shirazi, Masoud & Fuinhas, José Alberto, 2023. "Portfolio decisions of primary energy sources and economic complexity: The world's large energy user evidence," Renewable Energy, Elsevier, vol. 202(C), pages 347-361.
    10. Ki-Hong Choi & Seong-Min Yoon, 2020. "Asymmetric Dependence between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    11. Lijuan Huo & Jin Seo Cho, 2019. "Testing for the Sandwich-Form Covariance Matrix Applied to Quasi-Maximum Likelihood Estimation Using Economic and Energy Price Growth Rates," Working papers 2019rwp-152, Yonsei University, Yonsei Economics Research Institute.
    12. Bai, Xiwen & Kavussanos, Manolis G., 2022. "Hedging IMO2020 compliant fuel price exposure using futures contracts," Energy Economics, Elsevier, vol. 110(C).
    13. Shanghui Jia & Xinhui Chen & Liyan Han & Jiayu Jin, 2023. "Global climate change and commodity markets: A hedging perspective," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1393-1422, October.
    14. Perera, Indeewara & Silvapulle, Mervyn J., 2023. "Bootstrap specification tests for dynamic conditional distribution models," Journal of Econometrics, Elsevier, vol. 235(2), pages 949-971.
    15. Fen Li & Zhehao Huang & Junhao Zhong & Khaldoon Albitar, 2020. "Do Tense Geopolitical Factors Drive Crude Oil Prices?," Energies, MDPI, vol. 13(16), pages 1-20, August.
    16. Bai, Xiwen, 2021. "Tanker freight rates and economic policy uncertainty: A wavelet-based copula approach," Energy, Elsevier, vol. 235(C).
    17. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Estimating yield spreads volatility using GARCH-type models," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    18. Zheng, Kedi & Chen, Huiyao & Wang, Yi & Chen, Qixin, 2022. "Data-driven financial transmission right scenario generation and speculation," Energy, Elsevier, vol. 238(PC).
    19. Gong, Yuting & Li, Kevin X. & Chen, Shu-Ling & Shi, Wenming, 2020. "Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    20. Michail Nektarios A. & Melas Konstantinos D., 2022. "Geopolitical Risk and the LNG-LPG Trade," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 28(3), pages 243-265, September.
    21. Ngo Thai HUNG, 2020. "Conditional dependence between oil prices and CEE stock markets: a copula-GARCH approach Abstract: This study investigates both the constant and time-varying conditional dependency between crude oil a," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 11, pages 62-86, June.
    22. Basel Maraqa & Murad Bein, 2020. "Dynamic Interrelationship and Volatility Spillover among Sustainability Stock Markets, Major European Conventional Indices, and International Crude Oil," Sustainability, MDPI, vol. 12(9), pages 1-14, May.
    23. Yang, Dong & Wu, Lingxiao & Wang, Shuaian, 2021. "Can we trust the AIS destination port information for bulk ships?–Implications for shipping policy and practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    24. Bai, Xiwen & Lam, Jasmine Siu Lee & Jakher, Astha, 2021. "Shipping sentiment and the dry bulk shipping freight market: New evidence from newspaper coverage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    25. Siddiqui, Atiq W. & Basu, Rounaq, 2020. "An empirical analysis of relationships between cyclical components of oil price and tanker freight rates," Energy, Elsevier, vol. 200(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tong, Bin & Wu, Chongfeng & Zhou, Chunyang, 2013. "Modeling the co-movements between crude oil and refined petroleum markets," Energy Economics, Elsevier, vol. 40(C), pages 882-897.
    2. Albulescu, Claudiu Tiberiu & Aubin, Christian & Goyeau, Daniel & Tiwari, Aviral Kumar, 2018. "Extreme co-movements and dependencies among major international exchange rates: A copula approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 56-69.
    3. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    4. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    5. Zhu, Hui-Ming & Li, Rong & Li, Sufang, 2014. "Modelling dynamic dependence between crude oil prices and Asia-Pacific stock market returns," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 208-223.
    6. Rehman, Mobeen Ur & Katsiampa, Paraskevi & Zeitun, Rami & Vo, Xuan Vinh, 2023. "Conditional dependence structure and risk spillovers between Bitcoin and fiat currencies," Emerging Markets Review, Elsevier, vol. 55(C).
    7. Jiang, Kunliang & Ye, Wuyi, 2022. "Does the asymmetric dependence volatility affect risk spillovers between the crude oil market and BRICS stock markets?," Economic Modelling, Elsevier, vol. 117(C).
    8. Ngo Thai HUNG, 2020. "Conditional dependence between oil prices and CEE stock markets: a copula-GARCH approach Abstract: This study investigates both the constant and time-varying conditional dependency between crude oil a," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 11, pages 62-86, June.
    9. Bai, Xiwen & Lam, Jasmine Siu Lee, 2021. "Freight rate co-movement and risk spillovers in the product tanker shipping market: A copula analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    10. Avdulaj, Krenar & Barunik, Jozef, 2015. "Are benefits from oil–stocks diversification gone? New evidence from a dynamic copula and high frequency data," Energy Economics, Elsevier, vol. 51(C), pages 31-44.
    11. Grundke, Peter & Polle, Simone, 2012. "Crisis and risk dependencies," European Journal of Operational Research, Elsevier, vol. 223(2), pages 518-528.
    12. Bai, Xiwen, 2021. "Tanker freight rates and economic policy uncertainty: A wavelet-based copula approach," Energy, Elsevier, vol. 235(C).
    13. Barry K. Goodwin & Matthew T. Holt & Gülcan Önel & Jeffrey P. Prestemon, 2018. "Copula-based nonlinear modeling of the law of one price for lumber products," Empirical Economics, Springer, vol. 54(3), pages 1237-1265, May.
    14. Tiwari, Aviral Kumar & Adewuyi, Adeolu O. & Albulescu, Claudiu T. & Wohar, Mark E., 2020. "Empirical evidence of extreme dependence and contagion risk between main cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    15. Ngo Thai Hung, 2020. "Identifying the Dynamic Connectedness between Propane and Oil Prices: Evidence from Wavelet Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 315-326.
    16. Tong, Bin & Diao, Xundi & Wu, Chongfeng, 2015. "Modeling asymmetric and dynamic dependence of overnight and daytime returns: An empirical evidence from China Banking Sector," Economic Modelling, Elsevier, vol. 51(C), pages 366-382.
    17. Katja Ignatieva & Natalia Ponomareva, 2017. "Commodity currencies and commodity prices: modelling static and time-varying dependence," Applied Economics, Taylor & Francis Journals, vol. 49(15), pages 1491-1512, March.
    18. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    19. Silva Filho, Osvaldo Candido da & Ziegelmann, Flavio Augusto & Dueker, Michael J., 2012. "Modeling dependence dynamics through copulas with regime switching," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 346-356.
    20. Yue Peng & Wing Ng, 2012. "Analysing financial contagion and asymmetric market dependence with volatility indices via copulas," Annals of Finance, Springer, vol. 8(1), pages 49-74, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:78:y:2019:i:c:p:412-427. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.