Estimating GARCH models using support vector machines*

DOI10.1088/1469-7688/3/3/302zbMath1408.62151OpenAlexW2105671954MaRDI QIDQ4647256

Julio A. Afonso-Rodríguez, Javier Giner, Fernando Pérez-Cruz

Publication date: 14 January 2019

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1469-7688/3/3/302

zbMATH Keywords

regressionsupport vector machinesnonparametricGARCH models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (2)

Shape constrained risk-neutral density estimation by support vector regression ⋮ Conditional quantile change test for time series based on support vector regression

Cites Work

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• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Generalized autoregressive conditional heteroscedasticity
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
• ON THE SQUARED RESIDUAL AUTOCORRELATIONS IN NON-LINEAR TIME SERIES WITH CONDITIONAL HETEROSKEDASTICITY
• What good is a volatility model?
• The Distribution of Realized Exchange Rate Volatility
• Robust Estimation of a Location Parameter
• SVC-based equalizer for burst TDMA transmissions.
• Forecasting S\&P 100 volatility: The incremental information content of implied volatilities and high-frequency index returns

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