Quasi-maximum likelihood estimation of GARCH models in the presence of missing values

DOI10.1080/00949655.2018.1546860OpenAlexW2900486661MaRDI QIDQ5107326

Marcos Henrique Cascone, Luiz Koodi Hotta

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1546860

zbMATH Keywords

financial time seriesconditional expectation and varianceincomplete time seriesvolatility of aggregated returns

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84) Stochastic models in economics (91B70)

Related Items (1)

Estimating \(\operatorname{GARCH}(1, 1)\) in the presence of missing data

Uses Software

• astsa
• FinTS
• impute

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A computationally efficient method for vector autoregression with mixed frequency data
• Time series: Theory and methods
• Generalized autoregressive conditional heteroscedasticity
• A new nonlinear formulation for GARCH models
• Missing data in time series: a note on the equivalence of the dummy variable and the skipping approaches
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Least squares estimation of ARCH models with missing observations
• Analysis of Financial Time Series

This page was built for publication: Quasi-maximum likelihood estimation of GARCH models in the presence of missing values