Monte Carlo based Calibration of Stochastic Volatility Models

Based on the Heston Calibration Project, the University of Trier in collaboration with an industry partner developed an algorithm for the identification of the underlying parameters of stochastic volatility models, evaluated with Monte Carlo Simulation.

The evaluation of derivative prices via Monte Carlo Simulation may be the method of choice if flexibility and ease of implementation come into play. This is due to the fact that it can be programmed rather quickly and allows to easily switch the model dynamics, even if the dimension of the problem increases. The drawback, however, is the well-known slow convergence of the Monte Carlo method. In particular if the number of model parameters is large, the calibration may take several hours until convergence of the method is achieved. Hence it is desirable to speed up the implementation.

Publications:
Käbe,C., Maruhn,J.H., Sachs,E.W., Adjoint Based Monte Carlo Calibration of Financial Market Models
Finance and Stochastics, Volume 13, Issue 3, 2009
Giese, A.M., Kaebe,C. Maruhn,J.H. and Sachs,E.W. Efficient Calibration of Problems in Option Pricing
Proceeding of ICIAM Conference 2007, Zurich, Switzerland (to appear)

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