Mads Hebsgaard

I study how returns co-move across assets and over time, and how to estimate these links reliably from noisy, high-dimensional financial data. Better models of dependence help investors, pension funds, hedge funds and regulators see where risk is concentrated, how it can spill across sectors and markets, and how to construct portfolios more robustly.
Methodologically, my work focuses on high-dimensional covariance and precision matrix estimation, fragility-adjusted shrinkage, and tests of sector structure in equity portfolios. I use tools from statistics, time-series analysis, resampling and machine learning to build methods that are both theoretically grounded and practically implementable.
I am motivated by problems where technical improvements in estimation can have large ex-post effects, such as more stable portfolios, clearer communication of uncertainty and a more resilient financial system. I particularly enjoy programming, quantitative finance and numerical methods.