数据丰富模型中的风险
Risk in a Data-Rich Model
AI总结：
研究采用带随机波动率的动态因子模型，刻画美国百余个宏观金融变量的非对称尾部风险，发现其普遍存在且异质性具系统性，因子暴露解释了过半横截面变异，可反映经济脆弱性分布与尾部风险平衡的时间变化。
AI中文摘要：
我们使用带随机波动率的动态因子模型，刻画了美国一百多个宏观经济与金融变量的非对称尾部风险。单一机制将增长风险（Growth-at-Risk）、通胀风险（Inflation-at-Risk）与部门风险异质性统一起来：共同因子及其波动率协同变动，而异质性载荷将产生的非对称性不均匀地传递到各变量。我们发现非对称尾部风险普遍存在但具有异质性，且这种异质性是系统性的：因子暴露（尤其是对金融条件和通胀的暴露）解释了变量间尾部非对称性超过一半的横截面变异，这些暴露决定了经济中脆弱性的集中位置以及尾部风险的平衡随时间的变化情况。
英文摘要：
We characterize asymmetric tail risk across over one hundred U.S. macroeconomic and financial variables using a dynamic factor model with stochastic volatility. A single mechanism unifies growth-at-risk, inflation-at-risk, and sectoral risk heterogeneity: common factors and their volatilities move together, while heterogeneous loadings transmit the resulting asymmetry unevenly across variables. We find that asymmetric tail risk is pervasive but heterogeneous. The heterogeneity is systematic: factor exposures, especially to financial conditions and inflation, explain over half of the cross-sectional variation in tail asymmetry across variables. These exposures determine where in the economy vulnerabilities concentrate and how the balance of tail risks shifts over time.
