TY - JOUR
T1 - COVID-19 Pandemic and Indices Volatility
T2 - Evidence from GARCH Models
AU - Mamilla, Rajesh
AU - Kathiravan, Chinnadurai
AU - Salamzadeh, Aidin
AU - Dana, Léo Paul
AU - Elheddad, Mohamed
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/10/17
Y1 - 2023/10/17
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85175570641&partnerID=8YFLogxK
U2 - 10.3390/jrfm16100447
DO - 10.3390/jrfm16100447
M3 - Article
AN - SCOPUS:85175570641
SN - 1911-8066
VL - 16
JO - Journal of Risk and Financial Management
JF - Journal of Risk and Financial Management
IS - 10
M1 - 447
ER -