Risk Forecasting in Shipping Exchange‐Traded‐Fund ( ETF ) Markets
研究了基于运费衍生品的航运ETF（BDRY）的风险特性，评估了多种计量模型在风险价值（VaR）和预期亏损（ES）预测上的准确性，发现结合时变波动和厚尾分布的模型表现更优，而预测组合方法在VaR和ES上均表现稳健。
ABSTRACT This article examines the risk properties of freight‐derivative‐based exchange‐traded funds (ETFs), focusing on the Breakwave Dry Bulk Shipping ETF (BDRY), and evaluates the accuracy of Value‐at‐Risk (VaR) and Expected Shortfall (ES) forecasts across a range of econometric models. Motivated by the growing financialisation of shipping markets and the emergence of ETFs as accessible freight‐risk instruments, the study addresses a gap in the literature, which has largely focused on physical freight rates and derivatives rather than securitised exposures. Using daily data from 2020 to 2025, we implement Historical Simulation, GARCH‐type models with alternative distributions, Extreme Value Theory, and forecast combination methods, and assess performance using regulatory backtesting frameworks. The results show that models incorporating time‐varying volatility and heavy‐tailed distributions outperform simpler specifications, while forecast combination approaches consistently deliver strong performance across VaR and ES measures. ES forecasting remains particularly challenging, especially at extreme confidence levels, highlighting significant model risk. Evidence from stylised investment strategies confirms the economic value of accurate tail‐risk measurement. The findings have important implications for portfolio management and financial regulation, supporting the use of model averaging and robust backtesting in the context of derivative‐based ETFs and Basel III/IV risk frameworks.