Examining Volatility in Indian Markets: Insights from NIFTY 50 and India VIX

Understanding market volatility is crucial for investors and policymakers, especially in emerging markets like India. This study examines the relationship between Nifty 50 returns, India VIX, and market sentiment to determine whether past trends and investor sentiment can predict market fluctuations. The analysis begins with Granger causality tests, correlation analysis, and regression models to explore the predictive link between Nifty 50 and India VIX. To capture volatility patterns, GARCH modeling is applied, followed by ARIMA forecasting to estimate future market movements. Additionally, the broader machine-learning technique of Gradient Boosting is applied in conjunction with a volatility-adjustment based on momentum. Lastly, Natural Language Processing (NLP) on financial news headlines is used to assess how sentiment influences stock returns and volatility. By integrating these approaches, this research provides a comprehensive perspective on market behavior in India, combining statistical analysis with sentiment-driven insights. The findings suggest that Nifty 50 returns significantly impact India VIX, while sentiment analysis provides additional predictive power, offering insights into market behavior and risk perception.

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.52783/eel.v15i2.2820
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00
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Examining Volatility in Indian Markets: Insights from NIFTY 50 and India VIX

Market Dynamics and Volatility
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Examining Volatility in Indian Markets: Insights from NIFTY 50 and India VIX

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Abstract

Understanding market volatility is crucial for investors and policymakers, especially in emerging markets like India. This study examines the relationship between Nifty 50 returns, India VIX, and market sentiment to determine whether past trends and investor sentiment can predict market fluctuations. The analysis begins with Granger causality tests, correlation analysis, and regression models to explore the predictive link between Nifty 50 and India VIX. To capture volatility patterns, GARCH modeling is applied, followed by ARIMA forecasting to estimate future market movements. Additionally, the broader machine-learning technique of Gradient Boosting is applied in conjunction with a volatility-adjustment based on momentum. Lastly, Natural Language Processing (NLP) on financial news headlines is used to assess how sentiment influences stock returns and volatility. By integrating these approaches, this research provides a comprehensive perspective on market behavior in India, combining statistical analysis with sentiment-driven insights. The findings suggest that Nifty 50 returns significantly impact India VIX, while sentiment analysis provides additional predictive power, offering insights into market behavior and risk perception.

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Market Dynamics and Volatility
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