论文标题
超越波动性：特质分数风险的常见因素
Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks
论文作者
论文摘要
这项研究从公司级特质回报的横截面分位数中提取了潜在因素，并证明它们携带的信息是传统波动率措施所遗漏的信息。值得注意的是，暴露于较低的常见特质分数因素需要一个独特的风险溢价，而现有波动性，下行或与尾部相关的风险因素或特征无法解释。此外，我们证明了从返回分布中衍生出的因子结构（捕获其不对称特征）也具有有关总市场回报的预测能力。
This study extracts latent factors from the cross-sectional quantiles of firm-level idiosyncratic returns and demonstrates that they carry information that is missed by conventional volatility measures. Notably, exposure to the lower-tail common idiosyncratic quantile factor entails a distinctive risk premium that cannot be explained by existing volatility, downside or tail-related risk factors or characteristics. Furthermore, we demonstrate that factor structures derived from quantiles across the return distribution--which capture its asymmetric features--also possess predictive capabilities regarding aggregate market returns.
