Preprint Article Version 1 This version is not peer-reviewed

Modelling Market Volatility with Univariate GARCH Models: Evidence from Nasdaq-100

Version 1 : Received: 24 September 2019 / Approved: 25 September 2019 / Online: 25 September 2019 (09:09:06 CEST)

How to cite: Aliyev, F.; Ajayi, R.; Gasim, N. Modelling Market Volatility with Univariate GARCH Models: Evidence from Nasdaq-100. Preprints 2019, 2019090280 (doi: 10.20944/preprints201909.0280.v1). Aliyev, F.; Ajayi, R.; Gasim, N. Modelling Market Volatility with Univariate GARCH Models: Evidence from Nasdaq-100. Preprints 2019, 2019090280 (doi: 10.20944/preprints201909.0280.v1).

Abstract

This paper models and estimates the volatility of nonfinancial, innovative and hi-tech focused stock index, the Nasdaq-100, using univariate symmetric and asymmetric GARCH models. We employ GARCH, EGARCH and GJR-GARCH using daily data over the period January 4, 2000 through March 19, 2019. We find that the volatility shocks on the index returns are quite persistent. Furthermore, our findings show that the index has leverage effect, and the impact of shocks is asymmetric, whereby the impacts of negative shocks on volatility are higher than those of positive shocks of the same magnitude.

Subject Areas

volatility; risk, garch models; nasdaq-100

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