Skip to main content
SpringerLink
Log in

VaR as a risk management framework for the spot and futures tanker markets

  • Original Paper
  • Published:
Operational Research Aims and scope Submit manuscript

Abstract

The fluctuation of the freight rates is an important source of risk for all participants in the tanker shipping markets including ship-owners, charterers, traders, hedge funds, banks, etc. This study examines the freight rate risk involved in the most popular clean tanker route and the most popular dirty tanker route using historical prices from April 2008 to September 2015 for the routes TC5 and TD7 which are further divided into an in-sample period from 24 April 2008 to 7 November 2013, to estimate the coefficients and an out-of-sample period from 8 November 2013 to 2 September 2015, to measure the day to day Value at Risk performance. The analysis of the historical returns of both spot and future prices reveals historical distributions with high peaks and fat tails. The establishment of a risk management method that could capture these distribution characteristics is of paramount importance. For the quantification of the risk, the Value at Risk approach is applied. More specifically, a range of parametric (multiple GARCH family) and non-parametric (i.e. historical simulation) Value at Risk models are applied on the returns of both TC5 and TC7 spot and one and three months future markets. The results suggest substantial freight rate risk at both routes. The backtesting of the Value at Risk models is applied in two stages, firstly by the means of statistical accuracy of the results and secondly by the means of economic accuracy, in order to track down the best VAR models in the case of our research. According to the results, the simple GARCH and non-parametric models are proposed for risk management purposes, for both spot and future markets. The results are consistent for both long and short positions. According to the results, simple GARCH non-parametric models perform better in risk management, for both spot and futures markets. The results are consistent for both long and short positions.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. It should be mentioned that their conclusion that simpler risk measurement methods should be selected in preference to more complex methods for freight rates are compatible with the conclusions of our study.

  2. This is an index provided as a joint venture between two non-profit organizations, the Worldscale Association Limited (London) and the Worldscale Association Inc. (New York). Both companies are under control of a Management Committee, consisting of senior brokers from leading tanker broking firms in London and New York.

  3. If the quoted price for TD3 is 65 Worldscale points it means 65% of the flat rate. If the flat rate is 10 USD/mt per day it means that the actual price per metric ton is USD 10 * 65% = 6.5 USD/mt per day. In order to obtain the amount of freight this rate has to be multiplied by the total tons of the cargo. If an FFA contact is the case, then actual contract value results from the product of this rate with the lot size and the number of lots.

  4. For example, a Panamax vessel with a voyage from Middle East Gulf to North Asia will be fixed with a higher Worldscale points from a VLCC due to the economies of scale that must favor the charterer of the second one. Similarly, if the demand for tanker transportation decreases within a year, the Worldscale points will be decreased as well.

References

  • Abouarghoub W (2008) Measuring shipping tanker freight risk

  • Abouarghoub WM, Mariscal IBF (2011) Measuring level of risk exposure in tanker Shipping freight markets. Int J Bus Soc Res 1(1):20–44

    Google Scholar 

  • Afzal F, Haiying P (2021) Value-at-risk analysis for measuring stochastic volatility of stock returns: using GARCH-based dynamic conditional correlation model. SAGE Open. https://doi.org/10.1177/21582440211005758

    Article  Google Scholar 

  • Alizadeh A, Nomikos N (2009) Shipping derivatives and risk management. Palgrave Macmillan, London

    Book  Google Scholar 

  • Andriosopoulos K, Nomikos N (2013) Risk management in the energy markets and Value-at-Risk modelling: an hybrid approach. https://doi.org/10.1080/1351847X.2013.862173, pp. 548-574

  • Angabini A Wasiuzzaman S (2011) GARCH models and the financial crisis-a study of the malaysian stock market. Int J App Econ Fin 5(3):226–236. https://doi.org/10.3923/ijaef.2011.226.236

  • Angelidis T, Skiadopoulos G (2008) Measuring the market risk of freight rates: a value-at-risk approach. Int J Theor Appl Finance 11:447–469

    Article  Google Scholar 

  • Angelidis T, Benos A, Degiannakis S (2004) The use of GARCH models in VaR estimation. Stat Methodol 1(1):105–128

    Article  Google Scholar 

  • Argyropoulos C, Panopoulou E (2015) Measuring the market risk of freight rates: a forecast combination approach, Available at SSRN: https://ssrn.com/abstract=2211583 or http://dx.doi.org/https://doi.org/10.2139/ssrn.2211583

  • Baillie RT, Bollerslev T (1991) Intra-day and inter-market volatility in foreign 19 exchange rates. Rev Econ Stud 58:565–585

    Article  Google Scholar 

  • Bodie Z, Kane A, Marcus AJ (2009) Investments. Mc Graw Hill, New York

    Google Scholar 

  • Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econom 31:307–327

    Article  Google Scholar 

  • Bonett DG, Seier E (2002) A test of normality with high uniform power. Comput Stat Data Anal 40:435–445

    Article  Google Scholar 

  • Bookstaber RM, Pomerantz S (1989) An information-based model of market volatility. Finance Anal J 45:37–46

    Article  Google Scholar 

  • Bowman KO, Shenton LR (1975) Omnibus test contours for departures from normality based on b1 and b2. Biometrika 62:243–250

    Google Scholar 

  • Brooks C (2008) Introductory econometrics for finance. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Çanakoglu E, Adıyeke E (2020) Comparison of electricity spot price modelling and risk management applications. Energies 13:4698. https://doi.org/10.3390/en13184698

    Article  Google Scholar 

  • Danielsson J (2011) Financial risk forecasting. Wiley, London

    Google Scholar 

  • Ding Z, Granger CW, Engle RF (1993) A long memory property of stock market returns and a new model. J Empir Finance 1(1):83–106

    Article  Google Scholar 

  • Diongue AK, Guegan D, Wolff RC (2008) Wolf exact maximum likelihood estimation for the BL-GARCH model under elliptical distributed innovations, Centre National de la Resherche Scientifique, HAL Id: halshs-00270719 https://halshs.archives-ouvertes.fr/halshs-00270719

  • Diongue AK, Guegan D, Vignal B (2009) Forecasting electricity spot market prices with a k-factor GIGARCH process. Appl Energy 86(4):505–510

  • Engle R (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4):987–1007

    Article  Google Scholar 

  • Füss R, Adams Z, Kaiser DG (2008) The predictive power of value-at-risk models in commodity futures markets. J Asset Manag. https://doi.org/10.1057/jam.2009.21

    Article  Google Scholar 

  • Gel YR, Gastwirth JL (2008) A robust modification of the Jarque-Bera test of normality. Econ Lett 99:30–32

    Article  Google Scholar 

  • Gel YR, Miao W, Gastwirth JL (2007) Robust directed tests of normality against heavy-tailed alternatives. Comput Stat Camp Data Anal 51:2734–2746

    Article  Google Scholar 

  • Glosten L, Jagannathan R, Runkle D (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks. J Finance XL VIII:1779–1801

    Article  Google Scholar 

  • Goerlandt F, Montewka J (2015) A framework for risk analysis of maritime transportation systems: a case study for oil spill from tankers in a ship–ship collision. Saf Sci 76:42–66. https://doi.org/10.1016/j.ssci.2015.02.009

    Article  Google Scholar 

  • Gupta A, Rajib P (2018) Do VaR exceptions have seasonality? An empirical study on Indian commodity spot prices. IIMB Manag Rev 30(4):369–384. https://doi.org/10.1016/j.iimb.2018.05.008

    Article  Google Scholar 

  • Hagan PS, Kumar D, Lesniewski AS, Woodward DE (2002) Managing smile risk. WILMOTT Mag 1:84–108

    Google Scholar 

  • Haug EG (2007) The complete guide to option pricing formulas. The Mcgraw-Hill Companies Inc, New York

    Google Scholar 

  • Heston S (1993) A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev Financ Stud 6:327–343

    Article  Google Scholar 

  • Hull JC (2012) Options, futures and other derivatives. Pearson Education Limited, Harlow

    Google Scholar 

  • Jarque CM, Bera AK (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ Lett 6:255–259

    Article  Google Scholar 

  • Jensen A, Lange T (2007) Addressing the IGARCH puzzle, Department of Natural Sciences & Department of Mathematical Sciences, University of Copenhagen

  • Johansson A, Sowa V (2013) A comparison of GARCH models for VaR estimation in three different markets, Mathematics

  • Katsampoxakis I, Basdekis H, Anathreptakis K (2015) How firm and markets characteristics affect profitability: an empirical study. Int J Corp Finance Account (IJCFA) 2(1):2015. https://doi.org/10.4018/IJCFA.2015010104

    Article  Google Scholar 

  • Kavussanos MG, Dimitrakopoulos DN (2011) Market risk model selection and medium-term risk with limited data: application to ocean tanker freight markets. Int Rev Finance Anal 20:258–268

    Article  Google Scholar 

  • Konstantakis K, Papageorgiou T, Christopoulos A, Dokas I, Michaelides P (2019) Business cycles in Greek maritime transport: an econometric exploration (1998–2015). Oper Res Int J 19:1059–1079

    Article  Google Scholar 

  • Lopez JA (1998) Methods for evaluating Value-at-Risk estimates. Federal Reserve. Bank of New York. Economic Policy Review

  • Mandelbrot B (1963) The variation of certain speculative prices. J Bus 36:394–419

    Article  Google Scholar 

  • Matei M (2009) Assessing volatility forecasting models: why GARCH models take the lead. J Econ Forecast Inst Econ Forecast 0(4):42–65

    Google Scholar 

  • McAleer M (2005) Automated inference and learning in modeling financial volatility. Economet Theor 21:232–261

    Article  Google Scholar 

  • Nelson DB (1989) Modelling stock market volatility changes. In: Proceedings of the american statistical association, Business and Economic Statistics Section, pp 93–98

  • Nelson D (1990) ARCH mpdes as diffusion approximations. J Econ 45:7–38

    Article  Google Scholar 

  • Nelson D (1991) Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59(2):347–370

    Article  Google Scholar 

  • Orhan M, Koksal B (2012) A comparison of GARCH models for VaR estimation. Expert Syst Appl 39(3):3582–3592. https://doi.org/10.1016/j.eswa.2011.09.048

    Article  Google Scholar 

  • Pearson K (1895) Contributions to the mathematical theory of evolution. II. Skew variation in homogeneous material. Philos Trans R Soc Lond 186:343–414

    Article  Google Scholar 

  • Pearson K (1905) Das Fehlergesetz und Seine Verallgemeinerungen Durch Fechner und Pearson. A Rejoinder. Biometrika 4(1/2):169–212

    Article  Google Scholar 

  • Rehman A, Jian W, Khan N, Saqib R (2018) Major crops market risk based on value at risk model in P.R. China. Sarhad J Agric 34(2):435–442. https://doi.org/10.17582/journal.sja/2018/34.2.435.442

    Article  Google Scholar 

  • Ruppert D (1987) What Is kurtosis?: an influence function approach. Am Stat 41:1–5

    Google Scholar 

  • Smolović JC, Lipovina-Božović M, Vujošević S (2017) GARCH models in value at risk estimation: empirical evidence from the Montenegrin stock exchange. Econ Res Ekon Istraž. https://doi.org/10.1080/1331677X.2017.1305773

    Article  Google Scholar 

  • Stopford M (2009) Maritime economics 3e. Routledge, England

    Google Scholar 

  • Storti D, Vitale C (2003) BL-GARCH models and asymmetries in vitality. Stat Methods Appl 12:19–39. https://doi.org/10.1007/s10260-003-0048-0

    Article  Google Scholar 

  • Terasvirta T (2006) An introduction to univariate GARCH models. SSE/EFI Working papers in Economics and Finance, 646

  • Teräsvirta T (2009) An introduction to univariate GARCH models. In: Mikosch T, Kreiß J-P, Davis RA, Andersen TG (eds) Handbook of financial time series. Springer, Berlin Heidelberg, pp 17–42

    Chapter  Google Scholar 

  • Zhao LT, Meng I, Zhang YJ, Li YT (2018) The optimal hedge strategy of crude oil spot and futures markets: evidence from a novel method. Int J Finance Econ. https://doi.org/10.1002/ijfe.1656

    Article  Google Scholar 

  • Zivot E, Wang J (2005) Modeling financial time series with SPLUS. Springer, Berlin Heidelberg, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics, National & Kapodistrian University of Athens, Athens, Greece

    Charalampos Basdekis & Ioannis Katsampoxakis

  2. Department of Business Administration, University of the Aegean, Chios, Greece

    Apostolos Christopoulos

  3. School of Applied Mathematics & Physics, National Technical University of Athens, Athens, Greece

    Alexandros Gkolfinopoulos

Authors
  1. Charalampos Basdekis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Apostolos Christopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexandros Gkolfinopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ioannis Katsampoxakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ioannis Katsampoxakis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Figs. 1, 2, 3 and 4.

See Tables 1,

Table 2 Worldscale flat rate quoted as USD/mt per day
Full size table

2,

Table 3 Worldscale flat rate assumptions
Full size table

3,

Table 4 Presents Value at Risk results for TC5 route’s spot price returns
Full size table

4,

Table 5 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TC5 spot prices for long positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

5,

Table 6 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the Loss Function scores under each approach for the TC5 spot prices for short positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

6,

Table 7 Presents Value at Risk results for TC5 route’s FFA 1M price returns
Full size table

7,

Table 8 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TC5 FFA 1M prices for long positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

8,

Table 9 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TC5 FFA 1M prices for short positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

9,

Table 10 Presents Value at Risk results for TC5 route’s FFA 3M price returns
Full size table

10,

Table 11 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TC5 3M prices for long positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

11,

Table 12 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TC5 FFA 3M prices for short positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

12,

Table 13 Presents Value at Risk results for TD7 route’s spot price returns
Full size table

13,

Table 14 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TD7 spot prices for long positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

14,

Table 15 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TD7 spot prices for short positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

15,

Table 16 Represents Value at Risk results for TD7 route’s FFA 1M price returns
Full size table

16,

Table 17 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TD7 FFA 1M prices for long positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

17,

Table 18 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TD7 FFA 1M prices for short positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

18,

Table 19 Presents Value at Risk results for TD7 route’s FFA 3M price returns
Full size table

19,

Table 20 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the loss function scores under each approach for the TD7 3M prices for long positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

20 and

Table 21 Presents statistical tests of unconditional, independent and conditional coverage of the interval forecasts as well as the Loss Function scores under each approach for the TD7 3M prices for short positions, denoted by LRuc, LRind, LRcc and QLF, respectively
Full size table

21.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Basdekis, C., Christopoulos, A., Gkolfinopoulos, A. et al. VaR as a risk management framework for the spot and futures tanker markets. Oper Res Int J 22, 4287–4352 (2022). https://doi.org/10.1007/s12351-021-00673-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12351-021-00673-y

Keywords

Search

Navigation