COVID-19 Pandemic and Indices Volatility: Evidence from GARCH Models

Rajesh Mamilla, Chinnadurai Kathiravan (), Aidin Salamzadeh (), Leo Dana and Mohamed Elheddad
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Rajesh Mamilla: VIT Business School, Vellore Institute of Technology, Vellore 632014, India
Chinnadurai Kathiravan: VIT Business School, Vellore Institute of Technology, Vellore 632014, India
Mohamed Elheddad: Teesside University International Business School, Teesside University, Middlesbrough TS1 3BX, UK

JRFM, 2023, vol. 16, issue 10, 1-14

Abstract: This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on returns. The study makes several contributions to the existing literature. First, it uses advanced volatility forecasting models, such as ARCH and GARCH, to improve volatility estimates and anticipate future volatility. Second, it enhances the analysis of index return volatility. The study found that the COVID-19 period outperformed the pre-COVID-19 and overall periods. Since the Nifty Realty Index is the most volatile, Nifty Bank, Metal, and Information Technology (IT) investors reaped greater returns during COVID-19 than before. The study provides a comprehensive review of the volatility and risk of nine NSE indices. Volatility forecasting techniques can help investors to understand index volatility and mitigate risk while navigating these dynamic indices.

Keywords: COVID-19; GARCH; NSE index; returns; risk; volatility (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
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