Dynamic conditional score models with time-varying location, scale and shape parameters

Astrid Ayala and Szabolcs Blazsek
Authors registered in the RePEc Author Service: Alvaro Escribano

UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía

Abstract: We introduce new dynamic conditional score (DCS) models with time-varyinglocation, scale and shape parameters. For these models, we use the Student's-t, GED(general error distribution), Gen-t (generalized-t), Skew-Gen-t (skewed generalized-t),EGB2 (exponential generalized beta of the second kind) and NIG (normal-inverseGaussian) distributions. We show that the maximum likelihood (ML) estimates of thenew DCS models are consistent and asymptotically Gaussian. As an illustration, weuse daily log-return time series data from the S&P 500 index for period 1950 to 2016.We find that, with respect to goodness-of-fit and predictive performance, the DCSmodels with dynamic shape are superior to the DCS models with constant shape andthe benchmark AR-t-GARCH model.

Keywords: Dynamic; conditional; score; models; Score-driven; shape; parameters (search for similar items in EconPapers)
JEL-codes: C22 C52 C58 (search for similar items in EconPapers)
Date: 2017-07-01
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:25043

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