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Forecasting the density of asset returns

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  • Trino-Manuel Niguez
  • Javier Perote

Abstract

In this paper we introduce a transformation of the Edgeworth-Sargan series expansion of the Gaussian distribution, that we call Positive Edgeworth-Sargan (PES). The main advantage of this new density is that it is well defined for all values in the parameter space, as well as it integrates up to one. We include an illustrative empirical application to compare its performance with other distributions, including the Gaussian and the Student's t, to forecast the full density of daily exchange-rate returns by using graphical procedures. Our results show that the proposed function outperforms the other two models for density forecasting, then providing more reliable value-at-risk forecasts.

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Bibliographic Info

Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2004/479.

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Date of creation: Oct 2004
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Handle: RePEc:cep:stiecm:/2004/479

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Web page: http://sticerd.lse.ac.uk/_new/publications/default.asp

Related research

Keywords: Density forecasting; Edgeworth-Sargan distribution; probability integral transformations; P-value plots; VaR;

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Cited by:
  1. Del Brio, Esther B. & Ñíguez, Trino-Manuel & Perote, Javier, 2011. "Multivariate semi-nonparametric distributions with dynamic conditional correlations," International Journal of Forecasting, Elsevier, vol. 27(2), pages 347-364.

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