Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100

Fuzuli Aliyev,Richard Ajayi,Nijat Gasim

doi:10.1016/j.jeca.2020.e00167

Abstract

This paper models and estimates the volatility of nonfinancial, innovative and hi-tech focused stock index, the Nasdaq-100, using univariate asymmetric GARCH models. We employ 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. The financial implication of the findings for investors is that Nasdaq-100 index returns’ volatility exhibits clustering, and this permits investors to establish future positions in expectation of this characteristic.

Highlights

All stakeholders in financial markets, especially investors are concerned with risks of the assets they invest in
To the best of our knowledge, there are no studies in the literature that comprehensively investigate the dynamics of Nasdaq-100 daily index return volatility with symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity model (GARCH) models over a very long period of time, in this case 2000 – 2019
To the best of our knowledge, there are no studies in the literature that comprehensively investigate the dynamics of Nasdaq-100 daily index return volatility especially covering the period of 2008 financial crisis and its aftermath

Summary

Introduction

All stakeholders in financial markets, especially investors are concerned with risks of the assets they invest in. Modelling and predicting financial assets’ volatility is an important signal for investors to balance their portfolios. Volatility is a measure of the variance of returns on a time series of asset prices over a given period (Cizeau et al (1997)) and it quantifies the risk related to that asset. Financial asset returns are commonly characterized by volatility clustering, that is, extended periods of “violent” or high market volatility followed by a period of high volatility, and “calm" or low market volatility followed by period of low volatility (Tsay (2010)). Financial time series tend to exhibit negative skewness, excess kurtosis, and temporal persistence in conditional variance (Andersen eta al (2001)). Financial assets returns are observed to often have thicker tails than expected under normality. Some studies propose that these tails might be so thick as to have come from a Cauchy distribution, or other distributions with infinite moments (Mandelbrot (1963))

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Results

Conclusion