Measuring and Predicting Financial Risks from an Intrinsic Time Perspective

• Applicant: Professorin Dr. Roxana Halbleib
• Subject Area: Statistics and Econometrics
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 521314650

This project aims at improving the financial risk estimation and forecasting by tackling the basic step of sampling the financial data in a ”wise” way that is informative about the markets’ activity and riskiness, i.e. in intrinsic time. This step is central in developing risk models that are desired to be simple, but simultaneously be able to capture all anomalies of the financial markets and to accurately predict them. The focus of my research is on volatility and tail measures of the return distributions and it is both theoretical and applied. In particular, my approach is to exploit the richness of the information content of high-frequency (HF) data from multiple alternative (to the calendar one) time perspectives driven by various intensity measures of market’s activity and, thus, understand the price evolution of financial markets, identify the signals that predict turbulences and restore the true price information in order to increase the predictability of risks in a sustainable, mostly data-driven manner. Moreover, my research aims at evaluating the empirical usefulness of existing and new risk estimation and forecast approaches in real-data applications, especially during turbulent financial, economic and political times.

• DFG Programme: Research Grants