Factor Representing Portfolios in Large Asset Markets

We study the properties of mimicking portfolios in an intertemporal APT model, in which the conditional mean and covariance matrix of returns vary in an interdependent manner. We use a signal extraction approach, and relate the efficiency of (possibly) dynamic basis portfolios to mean square error minimisation. We prove that many portfolios converge to the factors as the number of assets increases, but show that the conditional Kalman filter portfolios are the ones with both minimum tracking error variability, and maximum correlation with the common factors. We also show that our conclusions are unlikely to change when using parameter estimates.

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