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Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming
Joint Authors
Abid, Fathi
Ben Hamida, Sana
Abdelmalek, Wafa
Source
Journal of Applied Mathematics and Decision Sciences
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-08-31
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
The volatility is a crucial variable in option pricing and hedging strategies.
The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting.
By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes.
Total and out-of-sample mean squared errors are used as forecasting's performance measures.
Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models.
Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.
American Psychological Association (APA)
Abdelmalek, Wafa& Ben Hamida, Sana& Abid, Fathi. 2009. Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming. Journal of Applied Mathematics and Decision Sciences،Vol. 2009, no. 2009, pp.1-19.
https://search.emarefa.net/detail/BIM-452210
Modern Language Association (MLA)
Abdelmalek, Wafa…[et al.]. Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming. Journal of Applied Mathematics and Decision Sciences No. 2009 (2009), pp.1-19.
https://search.emarefa.net/detail/BIM-452210
American Medical Association (AMA)
Abdelmalek, Wafa& Ben Hamida, Sana& Abid, Fathi. Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming. Journal of Applied Mathematics and Decision Sciences. 2009. Vol. 2009, no. 2009, pp.1-19.
https://search.emarefa.net/detail/BIM-452210
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-452210