Estimating Systematic Risk Using Time Varying Distributions
提出一个动态向量GARCH模型来估计时变贝塔，发现贝塔随时间变化且均值回归，但市场下跌时贝塔是否更高结论不一；静态模型会高估非系统性风险超过10%。
This article proposes a dynamic vector GARCH model for the estimation of time‐varying betas. The model allows the conditional variances and the conditional covariance between individual portfolio returns and market portfolio returns to respond asymmetrically to past innovations depending on their sign. Covariances tend to be higher during market declines. There is substantial time variation in betas but the evidence on beta asymmetry is mixed. Specifically, in 50% of the cases betas are higher during market declines and for the remaining 50% the opposite is true. A time series analysis of estimated time varying betas reveals that they follow stationary mean‐reverting processes. The average degree of persistence is approximately four days. It is also found that the static market model overstates non‐market or, unsystematic risk by more than 10%. On the basis of an array of diagnostics it is confirmed that the vector GARCH model provides a richer framework for the analysis of the dynamics of systematic risk.