Minimax estimation with random coefficients: Theory and application to stock returns

Abstract

Testing the reliability of the capital asset pricing model (CAPM) for various stock market returns is an important task in capital market research. In all previous studies, a common feature consists in the application of ordinary least squares or Bayesian methods when it comes to estimation of parameters. The Bayesian approach seems to be fairly intractable by practitioners whereas the OLS approach often yields imprecise and thus doubtful results. In this paper, the CAPM is estimated by approximate minimax techniques extended to a random coefficient regression model (RCR). The method turns out to be efficient from both the economical and computational point of view.