Can GARCH Models Capture Long-Range Dependence?
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John Maheu
This paper investigates if component GARCH models introduced by Engle and Lee(1999) and Ding and Granger(1996) can capture the long-range dependence observed in measures of time-series volatility. Long-range dependence is assessed through the sample autocorrelations, two popular semiparametric estimators of the long-memory parameter, and the parametric fractionally integrated GARCH (FIGARCH) model. Monte Carlo methods are used to characterize the finite sample distributions of these statistics when data are generated from GARCH(1,1), component GARCH and FIGARCH models. For several daily financial return series we find that a two-component GARCH model captures the shape of the autocorrelation function of volatility, and is consistent with long-memory based on semiparametric and parametric estimates. Therefore, GARCH models can in some circumstances account for the long-range dependence found in financial market volatility.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
- Article
- Can GARCH Models Capture Long-Range Dependence?
- Are Real Exchange Rates Nonlinear or Nonstationary? Evidence from a New Threshold Unit Root Test
- Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches
- The International CAPM and a Wavelet-Based Decomposition of Value at Risk
- Dual Long Memory in Inflation Dynamics across Countries of the Euro Area and the Link between Inflation Uncertainty and Macroeconomic Performance
- Forecasting Stock Market Volatility with Regime-Switching GARCH Models
Artikel in diesem Heft
- Article
- Can GARCH Models Capture Long-Range Dependence?
- Are Real Exchange Rates Nonlinear or Nonstationary? Evidence from a New Threshold Unit Root Test
- Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches
- The International CAPM and a Wavelet-Based Decomposition of Value at Risk
- Dual Long Memory in Inflation Dynamics across Countries of the Euro Area and the Link between Inflation Uncertainty and Macroeconomic Performance
- Forecasting Stock Market Volatility with Regime-Switching GARCH Models
