AbstractThe entropy principle yields, for a given set of moments, a density that involves the smallest amount of prior information. We first show how entropy densities may be constructed in a numerically efficient way as the minimization of a potential. Next, for the case where the first four moments are given, we characterize the skewness-kurtosis domain for which densities are defined. This domain is found to be much larger that for Hermite or Edgeworth expansions. Last, we show how this technique can be used to estimate a GARCH model where skewness and kurtosis are time varying.
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Bibliographic InfoPaper provided by HEC Paris in its series Les Cahiers de Recherche with number 709.
Length: 19 pages
Date of creation: 01 Feb 2000
Date of revision:
Entropy; Semi-nonparametric estimation; Time-Varying skewness and Kurtosis; Garch Model;
Find related papers by JEL classification:
- C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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