This study aims to apply a new hybrid approach to estimate volatility in neural network
option-pricing model. The analytical results also indicate that the new hybrid method can be …

This study integrated new hybrid asymmetric volatility approach into artificial neural
networks option-pricing model to improve forecasting ability of derivative securities price …

This study applies backpropagation neural network to forecast Taiwan stock index option
prices. Some typical option price indicators are chosen first, and then inputted into …

This work presents a novel neural network model for forecasting option prices using past
volatilities and other options market factors. Out of different approaches to estimating …

In this research, forecasting of the option prices of Nikkei 225 index futures is carried out
using backpropagation neural networks. Different results in terms of accuracy are achieved …

This study compares the performances of neural network and Black-Scholes models in
pricing BIST30 (Borsa Istanbul) index call and put options with different volatility forecasting …

In this paper, a Computational Intelligence Approach and more particularly a neural network
is used to learn from data on the Black-Scholes implied volatility. The implied volatility …

Volatility is an important element for various financial instruments owing to its ability to
measure the risk and reward value of a given financial asset. Owing to its importance …

Volatility forecasting is an important task for those associated with the financial markets, and
has occupied the attention of academics and practitioners over the last two decades. This …

This study applies backpropagation neural network for forecasting TXO price under different
volatility models, including historical volatility, implied volatility, deterministic volatility …