Score-driven asset pricing: Predicting time-varying risk premia based on cross-sectional model performance
提出一种新的参数方法估计具有动态风险溢价的线性因子定价模型，通过观测驱动的更新机制减少横截面预测误差，适用于预测变量未知或质量不确定的情况。
This paper proposes a new parametric approach for estimating linear factor pricing models with dynamic risk premia. Time-varying risk prices and exposures follow an observation-driven updating scheme that reduces the one-step-ahead prediction error from a cross-sectional factor model at the current observation. This agnostic approach is particularly useful in situations where predictors are unknown or of uncertain quality. Updating schemes for elliptically distributed returns are derived and propose cross-sectional regression errors as driving sequence for the parameter dynamics. Estimation and inference are performed by likelihood maximization. A simulation study confirms that the novel method is capable of filtering and predicting substantial risk price movements. The empirical performance of the method is illustrated by an application to a panel of equity portfolios.