Computational Risk and Asset Management Research Group (C-RAM)

The C-RAM (“Computational Risk and Asset Management”) research group develops data-driven methods to extract and interpret risk and asset management insights from financial, economic, and alternative data sources. Our work combines financial economic modeling with tools from machine learning, data science, and high-performance computing.

We study how uncertainty, information, and investor behavior are reflected in asset prices—across equities, options, and macro-financial variables. Recent projects include the model-free estimation of dividend risk premia from option and survey data, and the use of natural language processing to quantify the impact of ECB communications across asset classes.

Our group contributes to both academic research and applied work at the frontier of financial econometrics, with an emphasis on robustness, interpretability, and computational scalability. We engage in collaborative research within KIT and with partners across Europe, and provide supervision and mentorship to students working on Bachelor theses, Master research modules, and Ph.D. projects linked to the KABFI program.