Quantum Assisted Time Series Modelling for Volatility Prediction in Indian Stock Indices

Financial risk management faces the essential problem of forecasting stock market volatility because traditional econometric models including GARCH fail to demonstrate the complex market behavior which characterizes emerging markets. The research investigates how quantum computing functions as an innovative forecasting method through its assessment of actual volatility patterns observed in four primary Indian stock market indexes between 2015 and 2025. Researchers tested two hybrid quantum-classical systems which included a Variational Quantum Regressor (VQR) and a Quantum Kernel Support Vector Regression (QK-SVR) against traditional GARCH family models and Long Short-Term Memory (LSTM) networks. The results demonstrate that quantum-assisted models achieve better performance than both traditional methods and deep learning benchmarks throughout all market scenarios while maintaining stability during financial emergencies. The VQR model which included India VIX data achieved maximum performance because it decreased Root Mean Square Error (RMSE) by 90% when compared to GARCH and by 65% to 76% when measured against LSTM. The current Noisy Intermediate-Scale Quantum (NISQ) era allows hybrid quantum models to function as efficient and accurate forecasting instruments for emerging market predictions.

Keywords: Quantum Computing, Volatility Prediction, Time Series Modelling, Indian Stock Market, GARCH, Variational Quantum Regressor, LSTM, Emerging Markets.

Quantum Computing Volatility Prediction Time Series Modelling Indian Stock Market GARCH Variational Quantum Regressor LSTM Emerging Markets.