On the Effectiveness of a Hybrid Model for Volatility Prediction

Volatility prediction of the stock market has been a hot topic in economics and finance. However, it is challenging to forecast the volatility accurately due to the complex and dynamic nature of the stock market. It is observed that deep neural network architectures are universal approximators and effective for financial tasks; moreover, a single model is generally not found effective in solving the volatile nature of the input. Therefore, we propose a hybrid model that uniquely combines the strength of two state-of-the-art deep network models to estimate stock volatility precisely. The proposed hybrid model is evaluated on several real-world datasets covering two stock indexes and top volatile bank stock. The proposed algorithm surpasses the existing and state-of-the-art uni models and showcases its strength in effectively capturing the volatility of different stocks.