RT Journal Article
T1 Quasi-Maximum Likelihood estimation of Stochastic Volatility models
A1 Ruiz Ortega, Esther
AB Changes in variance or volatility over time can be modelled using stochastic volatility (SV) models. This approach is based on treating the volatility as an unobserved vatiable, the logarithm of which is modelled as a linear stochastic process, usually an autoregression. This article analyses the asymptotic and finite sample properties of a Quasi-Maximum Likelihood (QML) estimator based on the Kalman filter. The relative efficiency of the QML estimator when compared with estimators based on the Generalized Method of Moments is shown to be quite high for parameter values often found in empirical applications. The QML estimator can still be employed when the SV model is generalized to allow for distributions with heavier tails than the normal. SV models are finally fitted to daily observations on the yen/dollar exchange rate.
PB Elsevier
SN 0304-4076
YR 1994
FD 1994
LK https://hdl.handle.net/10016/4786
UL https://hdl.handle.net/10016/4786
LA eng
NO Publicado además en: Recent developments in Time Series,  2003,  vol. 2,  ISBN13: 9781840649512, pp. 117-134
DS e-Archivo
RD 22 may. 2024