Ai algorithms for fitting GARCH parameters to empirical financial data

DOI10.1016/j.physa.2022.127869OpenAlexW4283693138MaRDI QIDQ2162984

Sergey Savel'ev, Luke De Clerk

Publication date: 9 August 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2022.127869

zbMATH Keywords

artificial neural networksGARCHheteroskedasticityautocovarianceempirical data

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Cites Work

• Method of moments estimation of GO-GARCH models
• Boosting GARCH and neural networks for the prediction of heteroskedastic time series
• Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study
• Quasi-maximum likelihood estimator of Laplace \((1,1)\) for GARCH models
• Generalized autoregressive conditional heteroscedasticity
• Nonstationary generalised autoregressive conditional heteroskedasticity modelling for fitting higher order moments of financial series within moving time windows
• Modelling and forecasting the kurtosis and returns distributions of financial markets: irrational fractional Brownian motion model approach
• Quasi-maximum likelihood estimation in GARCH processes when some coefficients are equal to zero
• Applications of Gram–Charlier expansion and bond moments for pricing of interest rates and credit risk
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• A note on skewness and kurtosis adjusted option pricing models under the Martingale restriction*
• What good is a volatility model?
• Introduction to Econophysics

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