Financial Models of Interaction Based on Marked Point Processes and Gaussian Fields

TL;DRAbstract

This thesis deals with interaction phenomena in marked point processes, with particular attention being paid to the extreme value theory framework and with application to high-frequency financial data. While classical low-frequency financial returns are measured at equally spaced temporal locations, transaction data occur at random and irregularly spaced points in time. Due to possible dependencies with the traded prices, the pattern of trading times might already contain information about the price process. We propose different characteristics, referred to as conditional mean marks, to measure interaction effects in marked point processes, based on second-order moment measures. While for general models, these characteristics are analytically intractable, they can be calculated for suitable Poisson process based models. Applying standard estimators to real data, the models' ability to capture interaction effects can be verified. We conduct this comparison for a commonly-used GARCH mode

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