Modeling stock markets' volatility using GARCH models with normal, Student's \(t\) and stable Paretian distributions

DOI10.1007/s00362-007-0080-5zbMath1312.91094OpenAlexW1982487535WikidataQ126211625 ScholiaQ126211625MaRDI QIDQ840975

José Dias Curto, Gonçalo Nuno Tavares, José Castro Pinto

Publication date: 14 September 2009

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-007-0080-5

zbMATH Keywords

conditional heteroskedasticitynon-Gaussian distributions

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Economic time series analysis (91B84)

Related Items (12)

A Bayesian inference for time series via copula-based Markov chain models ⋮ Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis ⋮ Specification tests for the error distribution in GARCH models ⋮ Recursive computation of piecewise constant volatilities ⋮ Unnamed Item ⋮ A characteristic function-based approach to approximate maximum likelihood estimation ⋮ Methods for estimating the upcrossings index: improvements and comparison ⋮ Estimation under copula-based Markov normal mixture models for serially correlated data ⋮ Fourier inference for stochastic volatility models with heavy-tailed innovations ⋮ Asymptotic normality of the MLE in the level-effect ARCH model ⋮ Estimating the Population Coefficient of Variation by Confidence Intervals ⋮ Markov switching asymmetric GARCH model: stability and forecasting

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