This kind of minimalism is also required in modeling financial instruments ( as outlined in When Good ENUF is Great ) to receive enough reliable information to supporting deal decisions.
At the Frankfurt MathFinance Conference Andreas Binder will explain the danger of under-and-over-complexity in the important detail of model calibration of stochastic volatility models.
15-Mar-10, 4:00 pm, Messeturm Frankfurt,
Using Different Error Functionals in the Calibration of Stochastic Volatility Models
Stochastic volatility models and models including jump processes like the Heston and the Bates model gain more and more interest in the community. For practical purposes, it is essential that a fast and stable calibration routine is available. This calibration is quite frequently intrinsically instable due to the inverse problem nature of the taks. In this talk, we study the use of different error functionals (L1,L2 norm) and minimization algorithms (local and global) for solving the inverse problem. We also report the influence of the different parameter sets obtained on the price of exotic options.