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Empirical research and application of ARIMA-GJRGARCH model on effectively creating Forward Freight Agreement trading signals

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study examines the volatility of the forward freight agreement (FFA) time series in the dry bulk shipping market. Series pattern analysis is first performed to determine the volatility and the characteristics of the unique FFA price time series. It then applies the ARIMA-GJRGARCH model to the Capesize FFA time charter (C5TC) and specific voyage charter one-month contracts (C3, C5 and C7), creating long or short signals, which helps market participants with FFA trading or hedging. In this study, these signals are collected and used to calculate the profit and loss for a specific period. Finally, the model-based return results are compared with the common buy-and-hold strategy. The empirical result suggests that this methodology is effective in generating trading signals, especially in the volatile periods, providing traders with prompt warnings about imminent market shocks. The purpose of the study is to examine whether this volatility-focused method is efficient in modelling FFA time series, and it also provides a handy method that may help market players make more accurate predictions when volatile days arrive
Rocznik
Strony
52--59
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • 30 Woodfall Ave, Barnet, London, United Kingdom
Bibliografia
  • 1. Alizadeh, A., Thanopoulou, H. & Yip, T.L. (2017) Investors’ behavior and dynamics of ship prices: A heterogeneous agent model. Transportation Research Part E: Logistics and Transportation Review 106(10), pp. 98–114, doi: 10.1016/ j.tre.2017.07.012.
  • 2. Batchelor, R., Alizadeh, A. & Visvikis, I. (2007) Forecasting spot and forward prices in the international freight market. International Journal of Forecasting 23(1), pp. 101–114, doi: 10.1016/j.ijforecast.2006.07.004.
  • 3. Box, G.E.P. & Jenkins, G. (1970) Time series analysis, forecasting and control. San Francisco: Holden-Day.
  • 4. Chen, S., Meersman, H. & Voorde, E.V.D. (2010) Dynamic interrelationships in returns and volatilities between Capesize and Panamax markets. Maritime Economics & Logistics 12(1), pp. 65–90, doi: 10.1057/mel.2009.19.
  • 5. Chen, Y.-S. & Wang, S.-T. (2004) The empirical evidence of the leverage effect on volatility in international bulk shipping market. Maritime Policy & Management 31(2), pp. 109–124, doi: 10.1080/0308883042000208301.
  • 6. Ghalanos, A. & Kley, T. (2020) rugarch: Univariate GARCH Models. R package version 1.4-4.
  • 7. Glosten, L.R., Jagannathan, R. & Runkle, D.E. (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance 48(5), pp. 1779–1801, doi: 10.1111/j.1540- 6261.1993.tb05128.x.
  • 8. Kasimati, E. & Veraros, N. (2017) Accuracy of forward freight agreements in forecasting future freight rates. Applied Economics 50(7), pp. 743–756, doi: 10.1080/ 00036846.2017.1340573.
  • 9. 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 30(1), pp. 67–82.
  • 10. Kavussanos, M.G., Visvikis, I.D. & Batchelor, R.A. (2004) Over-the-counter forward contracts and spot price volatility in shipping. Transportation Research. Part E: Logistics and Transportation Review 40(4), pp. 273−296, doi: 10.1016/j.tre.2003.08.007.
  • 11. Kavussanos, M.G., Visvikis, I.D. & Dimitrakopoulos, D.N. (2014) Economic spillovers between related derivatives markets: The case of commodity and freight markets. Transportation Research Part E: Logistics and Transportation Review 68, pp. 79–102, doi: 10.1016/j.tre.2014.05.003.
  • 12. Konstantinos, D.M. & Nektarios, A.M. (2021) The relationship between commodity prices and freight rates in the dry bulk shipping segment: A threshold regression approach. Maritime Transport Research 2(1), 100025, doi: 10.1016/ j.martra.2021.100025.
  • 13. Papailias, F., Thomakos, D.D. & Liu, J. (2017) The Baltic Dry Index: cyclicalities, forecasting and hedging strategies. Empirical Economics 52, pp. 255–282, doi: 10.1007/ s00181-016-1081-9.
  • 14. Roar, A., Georg, M.S.A. & Ole, M.H. (2020) Baltic Exchange index changes and FFA hedging efficiency. Transportation Research Procedia 48(8), pp. 107–122, doi: 10.1016/j.trpro.2020.08.010.
  • 15. Ryan, J.A., Ulrich, J.M., Thielen, W., Teetor, P. & Bronder, S. (2020) quantmod: Quantitative Financial Modelling Framework. R package version 0.4.17.
  • 16. Sarkar, D., Andrews, F., Wright, K., Klepeis, N. & Murrell, P. (2020) lattice: Trellis Graphics for R. R package version 0.20-41.
  • 17. Tsouknidis, D.A. (2016) Dynamic volatility spillovers across shipping freight markets. Transportation Research Part E: Logistics and Transportation Review 91(7), pp. 90– 111, doi: 10.1016/j.tre.2016.04.001.
  • 18. Wuertz, D., Setz, T., Chalabi, Y. & Maechler, M. (2020) timeSeries: Financial Time Series Objects (Rmetrics). R package version 3062.100.
  • 19. Xu, H., Tao, B., Shu, Y. & Wang, Y. (2021) Long-term memory law and empirical research on dry bulks shipping market fluctuations. Ocean & Coastal Management 213, 105838, doi: 10.1016/j.ocecoaman.2021.105838.
  • 20. Xu, J.J., Yip, T.L. & Marlow, P.B. (2011) The dynamics between freight volatility and fleet size growth in dry bulk shipping markets. Transportation Research Part E: Logistics and Transportation Review 47(6), pp. 983–991, doi: 10.1016/j.tre.2011.05.008.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8a53f3e4-acce-443d-b550-49cd46ef7107
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