Economic conditions and portfolio tail risk: A probability-weighted simulation approach
提出一种基于预测经济状态概率的加权模拟方法（FEWS），用于生成多资产联合收益分布，提升尾部风险预测准确性，并缓解样本窗口长度选择的偏差-方差权衡。
Financial market volatility and cross-asset correlations vary substantially over the business cycle, yet widely used resampling methods for tail risk forecasting remain backward-looking and fail to incorporate forward-looking economic conditions. This is a critical limitation because economic conditions fundamentally shape market risk profiles. To address this gap, we propose Forward-looking Economic State Probability-Weighted Simulation (FEWS), a novel and scalable approach that generates the joint return distribution of multiple assets conditional on predicted economic state probabilities. FEWS retains the strengths of resampling methods and adapts simulations to forward-looking macroeconomic conditions, thereby enhancing forecasting accuracy and mitigating the bias–variance trade-off inherent in the choice of sample window length. FEWS also captures key stylized facts of asset returns—such as time-varying volatility and correlations, the leverage effect, and asymmetric tails and dependencies—and allows these features to vary across economic states. Out-of-sample tests on equity portfolios show that FEWS outperforms benchmark methods, delivering more accurate tail risk forecasts and reducing sensitivity to the choice of sample window length. • We propose FEWS, a resampling framework conditioned on future economic conditions. • FEWS simulates forward-looking joint return distributions for tail risk forecasting. • FEWS captures economic-state-contingent volatility, leverage, and correlation dynamics. • Out-of-sample tests on equity portfolios show FEWS outperforms benchmark methods. • FEWS alleviates the bias–variance trade-off in forecasting.