Option model calibration using a bacterial foraging optimization algorithm

被引：0
|
作者：
Dang, Jing [1 ,2 ]
Brabazon, Anthony [1 ]
O'Neill, Michael [1 ]
Edelman, David [2 ]
机构：
[1] Univ Coll Dublin, Nat Computing Res & Applicat Grp, Dublin 2, Ireland
[2] Univ Coll Dublin, Sch Business, Dublin 2, Ireland
来源：
APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS | 2008年 / 4974卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Bacterial Foraging Optimization (BFO) algorithm is a biologically inspired computation technique which is based on mimicking the foraging behavior of E.coli bacteria. This paper illustrates how a BFO algorithm can be constructed and applied to solve parameter estimation of a EGARCH-M model which is then used for calibration of a volatility option pricing model. The results from the algorithm are shown to be robust and extendable, suggesting the potential of applying the BFO for financial modeling.
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页码：113 / +
页数：3
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