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Forecasting stock market volatility using (non‐linear) Garch models
Author(s) -
Franses Philip Hans,
Van Dijk Dick
Publication year - 1996
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199604)15:3<229::aid-for620>3.0.co;2-3
Subject(s) - econometrics , autoregressive conditional heteroskedasticity , volatility (finance) , economics , stock market , heteroscedasticity , skewness , financial models with long tailed distributions and volatility clustering , stock market index , stock (firearms) , financial economics , forward volatility , implied volatility , geography , context (archaeology) , archaeology
In this paper we study the performance of the GARCH model and two of its non‐linear modifications to forecast weekly stock market volatility. The models are the Quadratic GARCH (Engle and Ng, 1993) and the Glosten, Jagannathan and Runkle (1992) models which have been proposed to describe, for example, the often observed negative skewness in stock market indices. We find that the QGARCH model is best when the estimation sample does not contain extreme observations such as the 1987 stock market crash and that the GJR model cannot be recommended for forecasting.