Estimating the Dynamics of Mutual Fund Alphas and Betas
开发了一个卡尔曼滤波模型来追踪共同基金因子载荷的动态变化，并检验能否事先识别具有市场择时能力的基金经理。研究发现，卡尔曼滤波模型在月度数据中能更准确地检测择时能力，且其预测的阿尔法和贝塔优于普通最小二乘模型。
This article develops a Kalman filter model to track dynamic mutual fund factor loadings. It then uses the estimates to analyze whether managers with market-timing ability can be identified ex ante. The primary findings are as follows: (i) Ordinary least squares (OLS) timing models produce false positives (nonzero alphas) at too high a rate with either daily or monthly data. In contrast, the Kalman filter model produces them at approximately the correct rate with monthly data; (ii) In monthly data, though the OLS models fail to detect any timing among fund managers, the Kalman filter does; (iii) The alpha and beta forecasts from the Kalman model are more accurate than those from the OLS timing models; (iv) The Kalman filter model tracks most fund alphas and betas better than OLS models that employ macroeconomic variables in addition to fund returns. The Author 2007. Published by Oxford University Press on behalf of The Society for Financial Studies., Oxford University Press.