Dynamic Hedging using Generated Genetic Programming Implied Volatility Models

Sana Ben Hamida (1, 2) , Fathi Abid (3) , Wafa Abdelmalek (3)

1 UPN SEGMI - Université Paris Nanterre - UFR Sciences économiques, gestion, mathématiques, informatique
2 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision
3 MODESFI

The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance of the best generated GP implied volatilities is tested in dynamic hedging and compared with Black-Scholes model. Based on MSE total, the dynamic training of GP yields better results than those obtained from static training with fixed samples. According to hedging errors, the GP model is more accurate almost in all hedging strategies than the BS model, particularly for in-the-money call options and at-the-money put options.

Mots clés: forecasting errors; dynamic hedging; implied volatility forecast; Genetic programming; hedging errors

Domaines: Intelligence artificielle [cs.AI]

Sana Ben Hamida, Fathi Abid, Wafa Abdelmalek. Dynamic Hedging using Generated Genetic Programming Implied Volatility Models. Genetic Programming - New Approaches and Successful Applications, InTech, 2012, ⟨10.5772/48148⟩. ⟨hal-02490809⟩