Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models
AbstractThe study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility. Copyright 2002 by the Eastern Finance Association.
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Bibliographic InfoArticle provided by Eastern Finance Association in its journal The Financial Review.
Volume (Year): 37 (2002)
Issue (Month): 1 (02)
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- Francesco Guidi, 2009.
"Volatility and Long-Term Relations in Equity Markets: Empirical Evidence from Germany, Switzerland, and the UK,"
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- Guidi, Francesco, 2008. "Volatility and Long Term Relations in Equity Markets: Empirical Evidence from Germany, Switzerland, and the UK," MPRA Paper 11535, University Library of Munich, Germany.
- Ezzat, Hassan, 2012. "The Application of GARCH and EGARCH in Modeling the Volatility of Daily Stock Returns During Massive Shocks: The Empirical Case of Egypt," MPRA Paper 50530, University Library of Munich, Germany.
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