Abstract:

Driven by a series of anti-globalization trade measures taken by the United States， as well as the sustained impacts of events such as the COVID-19 pandemic， the Russia-Ukraine war， and the Red Sea crisis， the resilience of the global maritime supply chain has increasingly faced severe challenges. The container liner freight rates， with an annual global freight volume exceeding one trillion U.S. dollars， have fluctuated significantly， bringing unprecedented risks to stakeholders in the global liner market.It aims to construct a risk measurement framework and model for liner shipping freight rate portfolios that match volatility characteristics. First， considering multiple typical characteristics of liner freight rate volatility， different pairing models are used to measure the freight rate risk of individual liner routes. Aiming at the volatility clustering， periodicity， and fat-tailedness of freight rates， three complementary CVaR （Conditional Value at Risk） models—AEG， PDF， and CAV—are employed to measure the risk exposure of container liner freight rates on single routes. Second， a CVaR risk measurement model for multi-route portfolios is constructed using implied tail correlation coefficients and covariance matrices. Although the popular Copula function can effectively quantify the non-linear correlation of “cut-off point” risk measurement tools such as variance and VaR， it ignores the impact of tail "average" risks （e.g.， CVaR and ES）， failing to accurately measure the non-linear relationships between multi-route freight rates caused by such tail “average” risks. Meanwhile， significant correlations exist between freight rates of different routes， especially in extreme situations， where fluctuations in one route often cause significant spillover effects. Third， based on the multi-route model， a multi-characteristic portfolio CVaR risk measurement model is developed using the minimum variance principle. Existing studies often focus on comparative analyses of single or multiple risk measurement models for shipping market risks， making it difficult to balance the accuracy and comprehensiveness of risk measurement. Weights are allocated to different multi-route CVaR models based on the minimum variance approach to construct a multi-characteristic composite CVaR risk measurement model.Taking the container liner spot freight market as the research target， four round-trip routes operated by a Chinese container liner company are selected： Far East-N.Europe， N.Europe-Far East， Far East-USWC， and USWC-Far East. The sample covers container freight rate data from May 2， 2018， to December 30， 2022， spanning 1，172 trading days.The research results show： First， the multi-route CVaR model demonstrates superior effectiveness in measuring the portfolio risk of liner freight rates， as it can effectively quantify risk exposure and better reveal the evolutionary patterns of freight rate risks， helping market stakeholders make more targeted management decisions. Second， the risk measurement of liner route freight rate portfolios based on multiple characteristics yields better results， with multi-characteristic CVaR models all reducing risks in the liner freight market， and the portfolio risk effect being optimal. Third， the portfolio optimization model， by assigning weights to CVaR results of different characteristics， can better mitigate risks in the liner market during periods of high volatility and provide more effective risk measurement results. Whether facing periods of severe volatility or extreme conditions in the liner freight market， the accuracy of risk exposure measurement exceeds 90%， and the probability of prediction failure under a 99% confidence level is less than one-third of that of single models. A theoretical reference for market stakeholders is provided to dynamically evaluate and monitor the portfolio risks of liner route freight rates.

Key words: line freight rate, container liner, volatility characteristics, combination risk measurement, CVaR model