
An extension of Heston's SV model to Stochastic Interest Rates
In 'A ClosedForm Solution for Options with Stochastic Volatility with A...
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On moments of exponential functionals of additive processes
Let X = (X t) t>0 be a realvalued additive process, i.e., a process wit...
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Dynamic Tail Inference with LogLaplace Volatility
We propose a family of stochastic volatility models that enable direct e...
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On moments of integral exponential functionals of additive processes
Let X = (X t) t>0 be a realvalued additive process, i.e., a process wit...
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HirschmanWidder densities
Hirschman and Widder introduced a class of Pólya frequency functions giv...
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Transfer function interpolation remainder formula of rational Krylov subspace methods
Rational Krylov subspace projection methods are one of successful method...
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Beating the curse of dimensionality in options pricing and optimal stopping
The fundamental problems of pricing highdimensional pathdependent opti...
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Correlators of Polynomial Processes
A process is polynomial if its extended generator maps any polynomial to a polynomial of equal or lower degree. Then its conditional moments can be calculated in closed form, up to the computation of the exponential of the socalled generator matrix. In this article, we provide an explicit formula to the problem of computing correlators, that is, computing the expected value of moments of the process at different time points along its path. The strength of our formula is that it only involves linear combinations of the exponential of the generator matrix, as in the onedimensional case. The framework developed allows then for easytoimplement solutions when it comes to financial pricing, such as for pathdependent options or in a stochastic volatility models context.
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