Title :
Generalized ant programming in option pricing: determining implied volatilities based on American put options
Author :
Keber, Christian ; Schuster, Matthias G.
Author_Institution :
Dept. of Bus. Adm., Univ. of Vienna, Austria
Abstract :
Generalized ant programming is a new method inspired by the genetic programming approach as well as by ant systems. It is applicable to all problems in which the search space of feasible solutions consists of computer programs. We use generalized ant programming to derive analytical approximations for determining the implied volatility based on American put options. Using experimental data as well as huge validation data sets we can show that the generalized ant programming based formulas for calculating implied volatilities deliver accurate approximation results and outperform other approximations presented in the literature.
Keywords :
artificial life; costing; financial data processing; genetic algorithms; stock markets; American put options; generalized ant programming; genetic programming; heuristics; implied volatilities; option pricing; validation data sets; Ant colony optimization; Biological neural networks; Brain modeling; Closed-form solution; Cost accounting; Genetic programming; Heuristic algorithms; Lattices; Pricing; Problem-solving;
Conference_Titel :
Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7654-4
DOI :
10.1109/CIFER.2003.1196251