Comparing the performances of symmetric and asymmetric generalized autoregressive conditionally heteroscedasticity models based on long-memory models under different distributions
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Publication:6172132
DOI10.1080/03610918.2021.1891432OpenAlexW3134049444MaRDI QIDQ6172132
Remal Shaher al-Gounmeein, Mohd Tahir Ismail
Publication date: 18 July 2023
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2021.1891432
hybrid modelasymmetric effectautoregressive fractionally integrated moving averagegeneralized autoregressive conditionally heteroscedasticitymodeling and forecastingsymmetric effect
Cites Work
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- An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series
- An empirical comparison of GARCH option pricing models
- Generalized autoregressive conditional heteroscedasticity
- Modeling volatility persistence of speculative returns: a new approach
- A FUNCTIONAL VERSION OF THE ARCH MODEL
- Functional Generalized Autoregressive Conditional Heteroskedasticity
- Distribution of the Estimators for Autoregressive Time Series With a Unit Root
- Long‐Memory Time Series
- Estimation Methods of the Long Memory Parameter: Monte Carlo Analysis and Application
- Testing for a unit root in time series regression
- Fractional differencing
- AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
- A Class of Nonlinear Arch Models
- Long-Term Memory in Stock Market Prices
- ESTIMATION OF THE FRACTIONAL DIFFERENCE PARAMETER IN THE ARIMA(p, d, q) MODEL USING THE SMOOTHED PERIODOGRAM
- A Test for Normality of Observations and Regression Residuals
- Volatility asymmetry in functional threshold GARCH model
- Fractionally integrated ARMA for crude palm oil prices prediction: case of potentially overdifference
- GARCH Models
- An analysis of variance test for normality (complete samples)
- Long memory and regime switching