Abstract
A Fast Fourier transform-based method for estimation of parameters of a stochastic process from a historical time series is proposed. The main advantage of this method is that allows to fit distributions that are defined in terms of characteristic function, instead of more commonly used density-based estimation. In turn more exotic models can be calibrated to the past data, thereby extending these exotic models to applications in risk management.
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