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Are Vector Autoregressions And Accurate Model For Dynamic Asset Allocation?

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  • Francisco Peñaranda

    ()
    (CEMFI, Centro de Estudios Monetarios y Financieros)

Abstract

Much of the growing literature on tactical and strategic asset allocation uses vector autoregressive models (VAR) for returns and predictors. Since the portfolio advice they generate may be misleading if those models are not an accurate description of reality, we evaluate the implied joint density forecasts of US monthly excess returns on stocks and bonds. From the point of view of an investor who rebalances monthly, a VAR offers a reasonable description of the data, which is not improved upon by richer models. We also study the relevance of considering time-varying risk premia and parameter uncertainty in density forecasts.

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Bibliographic Info

Paper provided by CEMFI in its series Working Papers with number wp2004_0419.

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Date of creation: Nov 2004
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Handle: RePEc:cmf:wpaper:wp2004_0419

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Keywords: Density forecasts; parameter uncertainty; portfolio choice; probability integral transform; risk premia.;

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