- Agent-based modeling (ABMs) in economics and finance
- Financial Econometrics and Machine Learning in finance
Current Projects
Environmental regulation risk and asset prices Working Paper January 2026
With H.P. Boswijk, C. Diks, S. Trimborn
We determine from market expectations how firms are affected by environmental regulatory risk. We use a text-based measure of environmental regulatory stringency derived from US EPA legal documents and industry-level relevance scores to capture time-varying regulatory risk exposure. We find that environmental regulatory stringency carries a positive and statistically significant risk premium, especially for firms with high cash holdings. For firms with low cash holdings, the effect is highly volatile, showing investors are uncertain about a firms future when faced with stricter regulation. Firms' environmental profiles further matter, as high-emission firms returns are negatively affected when regulatory stringency increases. Because regulatory text is released infrequently, real-time risk analysis is challenging. We derive a high-frequency, market-expectation-capturing Environmental Regulatory Risk Index (ERRI). We show that ERRI captures shifts in investors' expectations of environmental regulatory risk and how it reacts during environmental policy and political developments.
Modeling the Impact of Flood Risk on Residential Mortgage Default with an Agent-Based Model
With C. Diks, D. Kandhai, D. Roy