Deep Learning-Enabled Volatility Forecasting and Risk Optimization in Nifty Fifty Derivatives Market

Abstract

Forecasting volatility plays an important role in pricing derivatives, managing portfolios, and evaluating risks. In order to optimize the return on investments while reducing risks associated with the derivative markets, an accurate forecasting method for volatilities becomes vital for investors, banks and other financial institutions, and regulatory bodies. In this paper, a deep learning-based model for volatility prediction and portfolio risk optimization for Nifty Fifty derivatives market is proposed by considering historical NIFTY 50 index prices, India VIX, technical indicators, and market information from the derivatives market. Multiple models such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bi-directional LSTM (BiLSTM), CNN-LSTM, and Hybrid GARCH-LSTM model are compared based on their performance in forecasting volatilities. RMSE, MAE, MAPE, and R² are used to measure the forecasting power of the models. Meanwhile, Sharpe ratio, Sortino ratio, Maximum drawdown, and VaR are considered to evaluate investment efficiency. It is found that Hybrid GARCH-LSTM model achieves the best forecasting result due to its ability to model both volatility clustering and non-linear temporal dependencies. Furthermore, using the predicted volatilities for portfolio optimization can minimize investment risks and improve returns.