RT Generic
T1 Dynamic conditional score models with time-varying location, scale and shape parameters
A1 Ayala, Astrid
A1 Blazsek, Szabolcs
A1 Escribano, Álvaro
A2 Universidad Carlos III de Madrid. Departamento de Economía, 
AB 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.
SN 2340-5031
YR 2017
FD 2017-07-01
LK https://hdl.handle.net/10016/25043
UL https://hdl.handle.net/10016/25043
LA eng
DS e-Archivo
RD 5 jul. 2024