Time Varying Beta Risk: An Analysis of Alternative Modelling Techniques
比较了GARCH模型、扩展市场模型和卡尔曼滤波三种方法在估计时变贝塔上的表现，发现卡尔曼滤波在均方误差上最优，且组合方法可能更有效。
This paper investigates the performance of three different approaches to modelling time‐variation in conditional asset betas: GARCH models, the extended market model of Schwert and Seguin (1990) and the Kalman Filter algorithm. Using daily UK industry returns, we find the simple market model beta to be as efficient as the more complicated GARCH type models. However, the Kalman Filter algorithm incorporating a random walk parameterisation dominates all other models under the mean‐square error criterion. Finally, we provide strong evidence that a combination of the methods under investigation may lead to considerably more powerful estimators of the time‐variation in conditional beta.