Statistically efficient construction of \(a\)-risk-minimizing portfolio
Summary: We propose a semiparametrically efficient estimator for \(a\)-risk-minimizing portfolio weights. Based on the work of \textit{G. W. J. Bassett}, \textit{R. Koenker} and \textit{G. Kordas} [``Pessimistic portfolio allocation and choquet expected utility,'' J. Fin. Econometrics 2, No. 4, 477--492 (2004)], an \(a\)-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asymptotic normality are obtained. We apply the results of Hallin et al. (2008) to the problem of constructing \(a\)-risk-minimizing portfolios using residual signs and ranks and a general reference density. Monte Carlo simulations assess the performance of the proposed method. Empirical applications are also investigated.
scientific article; zbMATH DE number 6065405
scientific article