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<p>The tanker freight market is highly volatile and unpredictable, creating opportunities and risks for operators. The observation of historical data reveals that there are times of extreme deviations in freight rates from their mean values. These periods have not been thoroughly analyzed in the relevant maritime literature, and traditional modeling approaches do not account for the statistical properties of extreme values. This study employs Extreme Value Analysis (EVA) to model the stochastic behavior of tanker freight rates at unusually high levels. The Peak Over Threshold (POT) and Block Maxima approaches are tested, with the latter outperforming POT and adapting better to the data set. Following the Block-Maxima approach, the study estimates the probability of extreme values and uses this output to produce short-term forecasts of the Baltic Dirty Tanker Index (BDTI). The findings reveal the limitations of conventional risk models and offer a new statistical framework for modeling extreme freight rates, which can assist operational and investment decisions in the tanker sector.</p>

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An extreme value analysis framework for the tanker market

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月13日

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  • Yiannis Smirlis,
  • Vangelis Tsioumas,
  • Stratos Papadimitriou,
  • Ernestos Tzannatos

摘要

The tanker freight market is highly volatile and unpredictable, creating opportunities and risks for operators. The observation of historical data reveals that there are times of extreme deviations in freight rates from their mean values. These periods have not been thoroughly analyzed in the relevant maritime literature, and traditional modeling approaches do not account for the statistical properties of extreme values. This study employs Extreme Value Analysis (EVA) to model the stochastic behavior of tanker freight rates at unusually high levels. The Peak Over Threshold (POT) and Block Maxima approaches are tested, with the latter outperforming POT and adapting better to the data set. Following the Block-Maxima approach, the study estimates the probability of extreme values and uses this output to produce short-term forecasts of the Baltic Dirty Tanker Index (BDTI). The findings reveal the limitations of conventional risk models and offer a new statistical framework for modeling extreme freight rates, which can assist operational and investment decisions in the tanker sector.