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Mimicking portfolios, economic risk premia, and tests of multi-beta models
This paper considers two alternative formulations of the linear factor model (LFM) with nontraded factors. The first formulation is the traditional LFM, where the estimation of risk premia and alphas is performed by means of a cross-sectional regression of average returns on betas. The second formulation (LFM*) replaces the factors with their projections on the span of excess returns. This formulation requires only time-series regressions for the estimation of risk premia and alphas. We compare the theoretical properties of the two approaches and study the small-sample properties of estimates ...
Asset-pricing models and economic risk premia: a decomposition
The risk premia assigned to economic (nontraded) risk factors can be decomposed into three parts: (i) the risk premia on maximum-correlation portfolios mimicking the factors; (ii) (minus) the covariance between the nontraded components of the candidate pricing kernel of a given model and the factors; and (iii) (minus) the mispricing assigned by the candidate pricing kernel to the maximum-correlation mimicking portfolios. The first component is the same across asset-pricing models and is typically estimated with little (absolute) bias and high precision. The second component, on the other ...
Minimum-variance kernels, economic risk premia, and tests of multi-beta models
This paper uses minimum-variance (MV) admissible kernels to estimate risk premia associated with economic risk variables and to test multi-beta models. Estimating risk premia using MV kernels is appealing because it avoids the need to 1) identify all relevant sources of risk and 2) assume a linear factor model for asset returns. Testing multi-beta models in terms of restricted MV kernels has the advantage that 1) the candidate kernel has the smallest volatility and 2) test statistics are easy to interpret in terms of Sharpe ratios. The authors find that several economic variables command ...