Displaying similar documents to “Subordination, self-similarity, and option pricing.”

On the large deviations of a class of modulated additive processes

Ken R. Duffy, Claudio Macci, Giovanni Luca Torrisi (2011)

ESAIM: Probability and Statistics

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We prove that the large deviation principle holds for a class of processes inspired by semi-Markov additive processes. For the processes we consider, the sojourn times in the phase process need not be independent and identically distributed. Moreover the state selection process need not be independent of the sojourn times. We assume that the phase process takes values in a finite set and that the order in which elements in the set, called states, are visited is selected stochastically....

On the large deviations of a class of modulated additive processes

Ken R. Duffy, Claudio Macci, Giovanni Luca Torrisi (2012)

ESAIM: Probability and Statistics

Similarity:

We prove that the large deviation principle holds for a class of processes inspired by semi-Markov additive processes. For the processes we consider, the sojourn times in the phase process need not be independent and identically distributed. Moreover the state selection process need not be independent of the sojourn times. We assume that the phase process takes values in a finite set and that the order in which elements in the set, called states, are visited is selected stochastically....

Hazard rate model and statistical analysis of a compound point process

Petr Volf (2005)

Kybernetika

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A stochastic process cumulating random increments at random moments is studied. We model it as a two-dimensional random point process and study advantages of such an approach. First, a rather general model allowing for the dependence of both components mutually as well as on covariates is formulated, then the case where the increments depend on time is analyzed with the aid of the multiplicative hazard regression model. Special attention is devoted to the problem of prediction of process...

Additive Covariance kernels for high-dimensional Gaussian Process modeling

Nicolas Durrande, David Ginsbourger, Olivier Roustant (2012)

Annales de la faculté des sciences de Toulouse Mathématiques

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Gaussian Process models are often used for predicting and approximating expensive experiments. However, the number of observations required for building such models may become unrealistic when the input dimension increases. In oder to avoid the curse of dimensionality, a popular approach in multivariate smoothing is to make simplifying assumptions like additivity. The ambition of the present work is to give an insight into a family of covariance kernels that allows combining the features...