High-Frequency Interactions Between Market Volatility and Price Movements: Intraday Asymmetry Between India Vix and Nifty 50 Through Technical Lenses

Tuhin Mukherjee; Subhrajyoti Mandal

doi:10.69968/ijisem.2025v4i410-14

Authors

Tuhin Mukherjee University of Kalyani
Subhrajyoti Mandal University of Kalyani

DOI:

https://doi.org/10.69968/ijisem.2025v4i410-14

Keywords:

Technical analysis, Candlestick patterns, Intraday volatility, India VIX, Nifty 50

Abstract

This paper examines the intraday relationship between the Nifty 50 index and the India Volatility Index (India VIX) over a five-day trading period from 13th to 17th October 2025, using 15-minute OHLC data. Employing an integrated framework that combines technical candlestick analysis and statistical modelling, the study explores short-horizon volatility dynamics, sentiment behaviour, and market efficiency within the Indian equity market. The candlestick analysis reveals recurring reversal and continuation patterns—such as hammers, marubozu, and engulfing formations—closely associated with shifts in implied volatility. Regression and VAR results confirm a significant negative contemporaneous relationship between Nifty returns and VIX changes, with volatility responding almost instantaneously to price shocks. Evidence of asymmetric volatility behaviour demonstrates that India VIX reacts more sharply to market declines than to rallies, reflecting investor loss aversion. The findings validate the predictive value of traditional technical patterns in high-frequency contexts and underscore the role of India VIX as an effective intraday sentiment indicator. This research contributes to the literature on market microstructure and behavioural finance by linking visual market signals with econometric evidence in an emerging market setting.

References

Andersen, T. G., & Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance, 4(2-3), 115-158. https://doi.org/10.1016/S0927-5398(97)00004-2

Black, F. (1976). Studies of stock price volatility changes. Proceedings of the 1976 Meetings of the Business and Economic Statistics Section, American Statistical Association, 177-181.

Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47(5), 1731-1764. https://doi.org/10.1111/j.1540-6261.1992.tb04681.x

Carr, P., & Wu, L. (2006). A tale of two indices. Journal of Derivatives, 13(3), 13-29. https://doi.org/10.3905/jod.2006.616865

Chakrabarti, R., & Sarkar, S. (2019). An empirical analysis of the India VIX and Nifty 50 relationship: Evidence from the Indian derivatives market. Asian Journal of Finance & Accounting, 11(2), 23-41.

Christie, A. A. (1982). The stochastic behavior of common stock variances: Value, leverage and interest rate effects. Journal of Financial Economics, 10(4), 407-432. https://doi.org/10.1016/0304-405X(82)90018-6

Engle, R. F. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157-168. https://doi.org/10.1257/jep.15.4.157

French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3-29.

https://doi.org/10.1016/0304-405X(87)90026-2

Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.

https://doi.org/10.3905/jpm.2005.500363

Kumar, R., & Maheswari, M. (2021). India VIX and stock market returns: Empirical investigation of volatility dynamics. Global Business Review, 22(6), 1467-1484. https://doi.org/10.1177/0972150920924607

Park, C. H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), 786-826. https://doi.org/10.1111/j.1467-6419.2007.00519.x

Patel, N., Rathod, H., & Bhattacharya, M. (2018). Effectiveness of candlestick patterns in predicting stock price trends: Evidence from Indian stock market. IUP Journal of Applied Finance, 24(1), 47-62.

Sarwar, G. (2012). Is VIX an investor fear gauge in BRIC equity markets? Journal of Multinational Financial Management, 22(3), 55-65. https://doi.org/10.1016/j.mulfin.2012.01.002

Simon, D. P. (2003). The Nasdaq volatility index during and after the bubble. Journal of Derivatives, 11(2), 9-24.

https://doi.org/10.3905/jod.2003.319203

Singh, P., & Sharma, S. (2020). Intraday volatility dynamics in the Indian stock market: Evidence from high-frequency data. Indian Journal of Finance and Economics, 16(3), 211-228.

Tripathy, N., & Garg, S. (2016). Forecasting volatility in Indian stock market: A study using high-frequency data. Asia-Pacific Financial Markets, 23(3), 267-290. https://doi.org/10.1007/s10690-016-9203-9

Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17. https://doi.org/10.3905/jpm.2000.319728