We introduce a class of stock models that interpolates between
exponential Lévy models based on Brownian subordination and
certain stochastic volatility models with Lévy-driven
volatility, such as the Barndorff-Nielsen--Shephard model. The
driving process in our model is a Brownian motion subordinated to
a business time which is obtained by convolution of a Lévy
subordinator with a deterministic kernel. We motivate several
choices of the kernel that lead to volatility clusters while
maintaining the sudden extreme movements of the stock. Moreover,
we discuss some statistical and path properties of the models,
prove absence of arbitrage and incompleteness, and explain how to
price vanilla options by simulation and fast Fourier transform
methods.
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