The computational risk and asset management research group at the KIT focuses on developing risk management 4.0 solutions. Our research tries to understand how economic and financial risks affect financial markets and provides solutions for extracting relevant information from financial markets. Better information leads to improved automatic risk management which not only helps society to protect from adverse economic and financial shocks but also allows investors to secure "alpha" returns. As our teaching relies on the superb quantitative and information technology education of our esteemed colleagues at the Industrial-Engineering & Management department of the KIT, we tilt our research-oriented risk management education towards computational and applied aspects.
Our risk management philosophy for research, teaching and innovation relies on four focus areas. First, in order to measure and quantify financial market risks in real-time, we allocate a considerable amount of resources towards high-frequency pattern detection in publicly traded financial assets. Second, in order to analyse large amounts of financial data efficiently, a second strand of our resources is devoted towards the development of predictive analytics tools. Right now, we analyse the applicability of machine learning for algorithmic risk and asset management. Third, novel predictive analytics and big data tools require a smart IT infrastructure. We therefore incorporate cloud and high performance IT designs into our risk management 4.0 solutions. Last but not least, combining all of the previous three focus areas with our main research area, macro-finance, puts us in a position to analyze the interplay of economic and financial risks at a higher frequency and with modern predictive analytics tools.