RT Generic
T1 Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk
A1 Ayala, Astrid
A1 Blazsek, Szabolcs
A1 Escribano, Álvaro
A2 Universidad Carlos III de Madrid.  Departamento de Economía, 
AB 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.
SN 2340-5031
YR 2019
FD 2019-07-19
LK https://hdl.handle.net/10016/28638
UL https://hdl.handle.net/10016/28638
LA eng
NO Astrid Ayala and Szabolcs Blazsek acknowledge funding fromthe School of Business of Universidad Francisco Marroquín. Alvaro Escribano acknowledges fundingfrom 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).
DS e-Archivo
RD 30 jun. 2024