Learning about beta: Time-varying factor loadings, expected returns, and the conditional CAPM

被引:60
作者
Adrian, Tobias [2 ]
Franzoni, Francesco [1 ]
机构
[1] Univ Lugano, Swiss Finance Inst, CH-6904 Lugano, Switzerland
[2] Fed Reserve Bank New York, New York, NY 10045 USA
关键词
Beta; CAPM; Kalman filter; Anomalies; Value premium; MARKET-EFFICIENCY; TERM STRUCTURE; CROSS-SECTION; STOCK RETURNS; RISK PREMIA; TESTS; COVARIANCES; MODEL; VOLATILITY;
D O I
10.1016/j.jempfin.2009.02.003
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
We amend the conditional CAPM to allow for unobservable long-run changes in risk factor loadings. In this environment, investors rationally "learn" the long-run level of factor loadings from the observation of realized returns. As a consequence of this assumption, we model conditional betas using the Kalman filter. Because of its focus on low-frequency variation in betas, our approach circumvents recent criticisms of the conditional CAPM. When tested on portfolios sorted by size and book-to-market, our learning-augmented conditional CAPM passes the specification tests. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:537 / 556
页数:20
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