Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk

Ayala, Astrid; Blazsek, Szabolcs; Escribano, Álvaro

Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk

Autor(es): Ayala, Astrid; Blazsek, Szabolcs; Escribano, Álvaro

Editor: Universidad Carlos III de Madrid. Departamento de Economía

Fecha de edición: 2019-07-19

ISSN: 2340-5031

Patrocinador: Ministerio de Economía y Competitividad (España)

Agradecimientos: Astrid Ayala and Szabolcs Blazsek acknowledge funding from the School of Business of Universidad Francisco Marroquín. Alvaro Escribano acknowledges funding from the Spanish Ministry of Economy, Industry and Competitiveness (ECO2015-68715-R, ECO2016- 00105-001), Consolidation Grant (#2006/04046/002), and Maria de Maeztu Grant (MDM 2014-0431).

Serie/Num.: Working paper. Economics
19-12

Proyecto: Gobierno de España. ECO2015-68715-R
Gobierno de España. MDM 2014-0431

Palabras clave: Dynamic Conditional Score Models , Score-Driven Shape Parameters , Value-At-Risk , Outliers

Clasificación JEL: C22 , C52 , C58

URI: http://hdl.handle.net/10016/28638

Derechos: Atribución-NoComercial-SinDerivadas 3.0 España

Resumen:

Dynamic conditional score (DCS) models with time-varying shape parameters provide a exible method for volatility measurement. The new models are estimated by using the maximum likelihood (ML) method, conditions of consistency and asymptotic normality of ML ar [+]

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