For a specific inverse problem in option pricing, which is
focused on time-dependent functions of volatility and option
price data, we investigate the applicability of the method
of maximum entropy regularization including convergence and
convergence rates of regularized solutions. Due to the explicit
structure of the forward operator based on a generalized Black-Scholes formula the ill-posedness character of the nonlinear
identification problem under consideration can be verified in
detail. Numerical case studies illustrate the chances and limitations of the maximum entropy approach versus Tikhonov regularization for the specific problem.
Keywords:
maximum entropy regularization, nonlinear ill-posed problem,
volatility identification, inverse problem, option pricing,
convergence rates, numerical case studies