Stock Market Volatility Forecasting Models: A Comparative Study

13 Pages Posted: 18 May 2024

See all articles by Shweta Kundlia

Shweta Kundlia

Guru Gobind Singh Indraprastha University

Divya Verma Gakhar

Guru Gobind Singh Indraprastha University

Date Written: May 17, 2024

Abstract

Purpose: This paper aims to examine various stock market volatility forecasting models and identify the model which provides the most accurate forecasts. The variable under study is daily volatility of NIFTY 50 index returns for the period ranging from November 1995 to March 2016.

Design/methodology/approach: Eight forecasting models namely random walk model, historical mean model, moving averages model, exponential weighted moving averages model, exponential smoothing model, simple regression model, GARCH model and GJR-GARCH model are applied to forecast the NIFTY 50’s daily volatility. The forecasting error statistic tests the superiority of the models.

Findings: The ranking of any one forecasting model varies depending upon the choice of error statistic. The results show that the simple regression model is the best model to forecast the daily stock market volatility, followed by a random walk model and exponential smoothing model.

Originality: The subject of volatility in stock markets is of grave importance, especially in emerging markets like India. This paper suggests the models which should be used to forecast volatility most accurately in stock markets like India.

Keywords: volatility, forecasting models, GARCH, simple regression model

Suggested Citation

Kundlia, Shweta and Gakhar, Divya Verma, Stock Market Volatility Forecasting Models: A Comparative Study (May 17, 2024). Available at SSRN: https://ssrn.com/abstract=4832110 or http://dx.doi.org/10.2139/ssrn.4832110

Shweta Kundlia (Contact Author)

Guru Gobind Singh Indraprastha University ( email )

Divya Verma Gakhar

Guru Gobind Singh Indraprastha University ( email )

Sector 16 C,Dwarka
Dwarka
New Delhi, 110078
India

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