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Volatility Forecasting Techniques using Neural Networks: A Review
IJERTV10IS060333
Pranav B M , Dr. Vinay Hegde

Volatility is one of the key aspects in option pricing and considered as a risk associated with an asset. Because of its noisy, non-stationary, and heteroscedastic nature, predicting volatility for various forms of financial assets is one of the more mathematically challenging issues in time series prediction. Option risk management and trading depend heavily on the evaluation of option prices and implied volatility. The current studies use parametric models as a common strategy. But these models stand on a number of idealistic assumptions. Neural networks are widely used in all fields in recent years and their applicability includes the financial world as well. In this paper, existing neural network techniques in predicting volatility are studied. The paper covers mainly three types of neural networks- Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). These deep neural network models are compared with traditional models such as GARCH and its variants by using Root Squared Mean Error (RMSE) as the main loss function. It is observed that all neural network models perform much better than traditional models. But since most of these models depend only on historical data, more research is needed in considering market sentiment as a variable as it plays an important role in market fluctuations.