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Forecasting volatility of emerging stock markets: linear versus non‐linear GARCH models
Author(s) -
Gokcan Suleyman
Publication year - 2000
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 - 10.1002/1099-131x(200011)19:6<499::aid-for745>3.0.co;2-p
Subject(s) - autoregressive conditional heteroskedasticity , volatility (finance) , econometrics , economics , stock (firearms) , stock market , financial models with long tailed distributions and volatility clustering , emerging markets , linear model , financial economics , volatility smile , forward volatility , mathematics , statistics , finance , geography , context (archaeology) , archaeology
ARCH and GARCH models are substantially used for modelling volatility of time series data. It is proven by many studies that if variables are significantly skewed, linear versions of these models are not sufficient for both explaining the past volatility and forecasting the future volatility. In this paper, we compare the linear(GARCH(1,1)) and non‐linear(EGARCH) versions of GARCH model by using the monthly stock market returns of seven emerging countries from February 1988 to December 1996. We find that for emerging stock markets GARCH(1,1) model performs better than EGARCH model, even if stock market return series display skewed distributions. Copyright © 2000 John Wiley & Sons, Ltd.