- author = "Thomas H. Noe and Jun Wang",
- title = "The Self-Evolving Logic of Financial Claim Prices",
- booktitle = "Genetic Algorithms and Genetic Programming in Computational Finance",
- publisher = "Kluwer Academic Press",
- year = "2002",
- editor = "Shu-Heng Chen",
- chapter = "12",
- pages = "249--262",
- keywords = "genetic algorithms, genetic programming, Contingent Claims, Derivative Pricing",
- ISBN = "0-7923-7601-3",
-
URL = "
http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9",
-
DOI = "
doi:10.1007/978-1-4615-0835-9_12",
- abstract = "In this paper, we use Genetic Programming, an optimisation technique based on the principles of natural selection, to price financial contingent claims. Compared to the traditional arbitrage-based approach, this technique is useful when the underlying asset dynamics are unknown or when the pricing equations are too complicated to solve analytically. Comparing to other established data-driven option pricing techniques such as neural networks, implied binomial trees, etc., genetic programming has the advantage of not restricting the structure of the pricing formulae. In addition, because it is very easy to incorporate existing analytical pricing formulas into the evolutionary process, genetic programming can be applied in combination with existing pricing methods. In this paper, we show that genetic programming can recover Black-Sholes formula from a simulated data sample of fairly small size. The application to S&P 500 futures options also show promising results.",
-
notes = "part of \cite{chen:2002:gagpcf}
Author Affiliations: 2. A. B. Freeman School of Business, Tulane University New Orleans, LA, 70118-5669, USA 3. SAS Institute Inc., SAS Campus Drive, Room R5217, Cary, NC, 27513, USA",
