@article{MAKHILLIJSC201510621304,
    title = {Volatility Prediction Model for Option Pricing: A Soft Computing Approach},
    journal = {International Journal of Soft Computing},
    volume = {10},
    number = {6},
    pages = {391-399},
    year = {2015},
    issn = {1816-9503},
    doi = {ijscomp.2015.391.399},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2015.391.399},
    author = {Vijayalaxmi,Chandrashekara S.,H.G. and},
    keywords = {Prediction,volatility model,option pricing,artificial neural networks,determine},
    abstract = {Volatility is an important factor in the world of financial derivatives. Prediction of market volatility is very important for accurate valuation of stocks. This is required to calculate expected market return. Prediction of volatility is very much crucial in option pricing. Basically there are two main approaches to predict the volatility. They are historical approach and implied volatility approach. The main problem with the historical approach is that it pre assumes that future volatility will not change and that history will exactly repeat itself. Implied volatility claims that volatility on any day can only be estimated during trading on that day itself. In this study a sincere effort is made to predict and determine historical volatility using past data. Model works satisfactorily with minimum possible error.}
    }