Forecasting emerging equity market volatility using nonlinear GARCH models

Dick Van Dijk

doi:10.1016/S1474-6670(17)34766-3

Forecasting emerging equity market volatility using nonlinear GARCH models

Dick Van Dijk^*

^*Corresponding author for this work

Econometrics

Research output: Contribution to journal › Conference article › Popular

1 Citation (Scopus)

Abstract

In this paper we examine the usefulness of nonlinear Generalized Autoregressive Conditionally Heteroskedastic (GARCH) models for forecasting daily volatility in a number of Asian and Latin American emerging equity markets. Two of the most popular nonlinear GARCH specifications, the GJR model and the Exponential GARCH model, are found to outperform a linear GARCH model in terms of one-day ahead out-of-sample volatility forecasts. This conclusion holds both when volatility forecasts are evaluated by means of traditional criteria that rely upon a proxy for unobserved volatility or by means of indirect probability forecasts.

Original language	English
Pages (from-to)	221-226
Number of pages	6
Journal	IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume	36
Issue number	16
DOIs	https://doi.org/10.1016/S1474-6670(17)34766-3
Publication status	Published - 2003
Event	13th IFAC Symposium on System Identification, SYSID 2003 - Rotterdam, Netherlands Duration: 27 Aug 2003 → 29 Aug 2003

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© 2003 International Federation of Automatic Control.

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10.1016/S1474-6670(17)34766-3

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title = "Forecasting emerging equity market volatility using nonlinear GARCH models",

abstract = "In this paper we examine the usefulness of nonlinear Generalized Autoregressive Conditionally Heteroskedastic (GARCH) models for forecasting daily volatility in a number of Asian and Latin American emerging equity markets. Two of the most popular nonlinear GARCH specifications, the GJR model and the Exponential GARCH model, are found to outperform a linear GARCH model in terms of one-day ahead out-of-sample volatility forecasts. This conclusion holds both when volatility forecasts are evaluated by means of traditional criteria that rely upon a proxy for unobserved volatility or by means of indirect probability forecasts.",

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AB - In this paper we examine the usefulness of nonlinear Generalized Autoregressive Conditionally Heteroskedastic (GARCH) models for forecasting daily volatility in a number of Asian and Latin American emerging equity markets. Two of the most popular nonlinear GARCH specifications, the GJR model and the Exponential GARCH model, are found to outperform a linear GARCH model in terms of one-day ahead out-of-sample volatility forecasts. This conclusion holds both when volatility forecasts are evaluated by means of traditional criteria that rely upon a proxy for unobserved volatility or by means of indirect probability forecasts.

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ER -

Forecasting emerging equity market volatility using nonlinear GARCH models

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