Abstract
We present a new and easy-to-implement sequential sampling method for CGMY processes with either finite or infinite variation, exploiting the time change representation of the CGMY model and a decomposition of its time change. We find that the time change can be decomposed into two independent components. While the first component is a \emph{finite} \emph{generalized gamma convolution} process whose increments can be sampled by either the exact double CFTP (``coupling from the past'') method or an approximation scheme with high speed and accuracy, the second component can easily be made arbitrarily small in the $L^1$ sense. Simulation results show that the proposed method is advantageous over two existing methods under a model calibrated to historical option price data.
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