EGARCH models with fat tails, skewness and leverage

DOI10.1016/j.csda.2013.09.022zbMath1506.62076OpenAlexW2204730588MaRDI QIDQ1623534

Y. Aharonov

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://www.repository.cam.ac.uk/handle/1810/245327

zbMATH Keywords

scorevolatilityleverageStudent's \(t\)general error distributionheteroskedasticitytwo components

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Point estimation (62F10)

Related Items (12)

Portmanteau test for the asymmetric power GARCH model when the power is unknown ⋮ Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling ⋮ A dynamic conditional score model for the log correlation matrix ⋮ Tail behavior and dependence structure in the APARCH model ⋮ Inference for asymmetric exponentially weighted moving average models ⋮ GTL regression: a linear model with skewed and thick-tailed disturbances ⋮ Volatility Modeling with a GeneralizedtDistribution ⋮ Goodness-of-fit tests for Log-GARCH and EGARCH models ⋮ Model-based fuzzy time series clustering of conditional higher moments ⋮ Time‐series models with an EGB2 conditional distribution ⋮ Comparison of value-at-risk models using the MCS approach ⋮ Maximum likelihood estimation for score-driven models

Uses Software

• betategarch

Cites Work

• Properties and estimation of asymmetric exponential power distribution
• A generalized asymmetric Student-\(t\) distribution with application to financial econometrics
• Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: a stochastic recurrence equations approach
• Estimating the dimension of a model
• An automatic portmanteau test for serial correlation
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• Joint modelling of location and scale parameters of the t distribution
• Large-Sample Inference for the Epsilon-Skew-t Distribution
• The stochastic equation Y_n+1=A_nY_n + B_n with stationary coefficients
• On Bayesian Modeling of Fat Tails and Skewness
• The likelihood function of stationary autoregressive-moving average models
• ASYMPTOTIC INFERENCE FOR NONSTATIONARY GARCH

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