This paper investigates three techniques for the estimation of conditional time‐dependent betas: (a) a multivariate generalised ARCH approach; (b) a time‐varying beta market model approach suggested by Schwert and Seguin (1990); and (c) the Kalman filter technique. These approaches are applied to a sample of returns on Australian industry portfolios over the period 1974–1996. The evidence found in this paper, based on in‐sample forecast errors, overwhelmingly supports the Kalman filter approach When out‐of‐sample forecasts are considered the evidence again finds in favour of the Kalman filter approach.
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