DyMoDiF - Dynamic Models for the Digital Finance

The recent availability of large digital finance datasets brings new challenges to quantitative finance. Many of the classical financial econometric or optimization models become inappropriate or intractable when applied to digital finance data. Vast quantities of information available in every moment require improvement of the classical methodology, in order to understand correctly the information hidden in the data, as well as to model and predict any dynamic behavior. The project will contribute to the debate, and propose new methodologies, which will take the high-frequency structure of the data as the advantage and turn the „curse of dimensionality“ into „blessing of dimensionality“. In particular, new models for the dynamic risk measuring, optimal decision making and advanced asset pricing will be developer, analyzed and implemented. These models will help to better understand and explain the complex changes in financial world induced by upcoming digital age.