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

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: 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 are presented, and Monte Carlo simulation experiments are used to study the precision of ML. Daily data from the Standard & Poor's 500 (S&P 500) for the period of 1950 to 2017 are used. The performances of DCS models with constant and dynamic shape parameters are compared. In-sample statistical performance metrics and out-of-sample value-at-risk backtesting support the use of DCS models with dynamic shape.

Keywords: Dynamic; Conditional; Score; Models; Score-Driven; Shape; Parameters; Value-At-Risk; Outliers (search for similar items in EconPapers)
JEL-codes: C22 C52 C58 (search for similar items in EconPapers)
Date: 2019-07-19
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:28638

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