1. – Department Of Commerce, Gargi College, University Of Delhi, Delhi, India.
2. – Department Of Commerce, Gargi College, University Of Delhi, Delhi, India.
3. – Department Of Commerce, Gargi College, University Of Delhi, Delhi, India.
4. – Department Of Commerce, Gargi College, University Of Delhi, Delhi, India.
| Received
26-Dec-2022 |
Accepted
- |
Published
26-Dec-2022 |
In a first of its kind, this paper discovers the long-run relationship between investor sentiment and Indian stock market volatility over the period 2010 to 2021, using monthly data. Twenty-two variables have been identified and used as proxy for investor sentiment. Then, using principal component analysis, the first 11 principal components with an eigen value of more than one have been selected and used as sentiment sub-indices, which represent the sentiments of Indian investors. The volatility of the S&P BSE 500 index has been measured using the GARCH model. We have applied the auto-regressive distributed lag (ARDL) model to document the relationship between sentiment and volatility. Further analysis has been done using ARDL modelling, by taking sentiment sub-indices as independent variables and the S&P BSE 500 Index GARCH volatility as the dependent variable. The value of was found to be 0.752. The results show that most of the sentiment sub-indices have a negative impact on the Indian stock market volatility in the long run. Thus, it can be concluded that in the long run, when sentiment is positive, volatility is negative, and vice versa. This study would be a valuable addition to the existing body of literature on the subject, besides being useful to regulators, policy makers, and investors. Regulators and policy makers should watch out for the impact of fluctuations in selected sentiment sub-indices on volatility in the stock market. Investors can search for arbitrage opportunities in the market on the basis of selected indices.