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HOME / ENGINEERING AND TECHNOLOGY / JOURNAL OF ENGINEERING AND APPLIED SCIENCES (JEAS) / Volume 36 / ISSUE NO 1

COMPARISON OF ASYMMETRIC GARCH MODELS WITH ARTIFICIAL NEURAL NETWORK FOR STOCK MARKETS PREDICTION, A CASE STUDY

Samreen Fatima

Volume 36, Issue No 1, 2017, JOURNAL OF ENGINEERING AND APPLIED SCIENCES (JEAS)

Abstract

Much efforts have been done for modeling of financial data theoretically and empirically for the international

Keyword(s)

A-GARCH (Asymmetric GARCH),  EGARCH (Exponential GARCH),  PGARCH (Power GARCH),  ANN (Artificial neural networks)

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