Volatility prediction model for option pricing: A soft computing approach

Vijayalaxmi, Chandrashekara S. Adiga, H. G. Joshi, S. V. Harish

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)391-399
Number of pages9
JournalInternational Journal of Soft Computing
Volume10
Issue number6
DOIs
Publication statusPublished - 2015

Fingerprint

Soft computing
Soft Computing
Option Pricing
Volatility
Prediction Model
Costs
Implied Volatility
Derivatives
Financial Derivatives
Predict
Prediction
Valuation
Calculate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Modelling and Simulation

Cite this

Vijayalaxmi ; Adiga, Chandrashekara S. ; Joshi, H. G. ; Harish, S. V. / Volatility prediction model for option pricing : A soft computing approach. In: International Journal of Soft Computing. 2015 ; Vol. 10, No. 6. pp. 391-399.
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Volatility prediction model for option pricing : A soft computing approach. / Vijayalaxmi; Adiga, Chandrashekara S.; Joshi, H. G.; Harish, S. V.

In: International Journal of Soft Computing, Vol. 10, No. 6, 2015, p. 391-399.

Research output: Contribution to journalArticle

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