(https://w.afbcs.cn/TM44q3).

The dry bulk shipping market plays a crucial role in global trade. To examine the volatility, correlation, and risk spillover between freight rates in the BCI and BPI markets, this paper employs the GARCH-Copula-CoVaR model. We analyze the dynamic behavior of the secondary market freight index for dry bulk cargo, highlighting its performance in a complex financial environment and offering empirical support for the shipping industry and financial markets. The findings reveal that: (1) There are significant differences in correlation across various routes, with the correlation between BCI and BPI routes fluctuating over time. Among all route combinations, C5 and P3A_03 exhibit the highest positive correlation. (2) A one-way risk spillover exists between P1A_03 an C5, while two-way positive risk spillover is observed between other routes. This suggests that when a risk materializes on a specific route, other routes are also exposed to potential risks, with varying intensities of spillover. (3) The distance and geographical location of routes may be key factors influencing the differing intensities of risk spillover. This highlights the need to consider the geographical characteristics of routes in understanding risk transmission. This paper aims to provide risk management strategies based on these empirical findings, assisting shipping companies and investors in developing more effective responses to market volatility.干散货航运市场在全球贸易中扮演着至关重要的角色。为探究波罗的海好望角型船运价指数（BCI）与波罗的海巴拿马型船运价指数（BPI）市场中运价的波动性、相关性及风险溢出效应，本文采用GARCH-Copula-CoVaR模型。本文对干散货二级市场运价指数的动态特征展开分析，阐明其在复杂金融环境中的运行表现，为航运业与金融市场提供实证支撑。研究结果显示：（1）不同航线间的相关性存在显著差异，BCI与BPI各航线间的相关性随时间动态波动。在所有航线组合中，C5与P3A_03呈现出最高的正相关性。（2）P1A_03与C5之间存在单向风险溢出，而其余航线间则呈现双向正向风险溢出。这表明当某一特定航线发生风险事件时，其他航线也将面临潜在风险，且溢出强度存在差异。（3）航线的距离与地理位置或为影响风险溢出强度差异的关键因素，这凸显了在解析风险传导机制时，需纳入航线地理特征的必要性。本文旨在基于上述实证结果提出风险管理策略，助力航运企业与投资者制定更有效的市场波动应对方案。