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Bayesian inference for integer-valued Lévy processes with Non-Gaussian Ornstein-Uhlenbeck volatility modelling

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If you have a question about this talk, please contact Zoubin Ghahramani.

short talk (30 mins) - NOTE TIME CHANGE

Financial data are usually modelled as continuous, often involving geometric Brownian motion with drift, leverage and possibly jump components. A more practical description enlightens the discrete nature of the financial observations, as integer multiples of a fixed quantity, the ticksize, the monetary value associated with a single change during the evolution of the price. The class of integer-valued Lévy processes is used to model the observations, yielding desirable flexibility in the choice of the marginal distribution for a fixed time interval. Time deformation is achieved including stochastic volatility as Non-Gaussian Ornstein-Uhlenebeck processes. Bayesian inference is performed on application to real stock market indices.

This talk is part of the Machine Learning @ CUED series.

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