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

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.

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Notes

  1. 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. 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. 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. 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.

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Appendix

Appendix

See Figs. 1, 2, 3 and 4.

See Tables 1,

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

2,

Table 3 Worldscale flat rate assumptions

3,

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

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

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

6,

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

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

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

9,

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

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

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

12,

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

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

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

15,

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

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

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

18,

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

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

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

21.

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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 (2021). https://doi.org/10.1007/s12351-021-00673-y

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Keywords

  • VaR
  • GARCH
  • Tanker shipping market
  • Clean tanker route
  • Dirty tanker route
  • Papametric and non-parametric models