Observational Unit in Portfolio Data
In portfolio-based estimation, data granularity is not merely a measurement detail but part of the structural mapping.
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I am a Finance PhD candidate at INSEAD.
Before academia, I co-founded two companies: one failed rather miserably, and the other raised more venture capital than customers. Those experiences left me curious about how investors think, how capital gets allocated, and why markets so often refuse to behave as I naively expect them to.
That led me to a period of quiet contemplation at an asset management firm, and eventually to pursuing a PhD.
I am currently interested in asset pricing, portfolio choice, and financial econometrics.
Observational Unit in Portfolio Data: Implications for Asset Demand Estimation
I show that in logit demand systems, dispersion in mandate level primitives does not average out, but enters the consolidated demand object through higher-cumulant curvature. Moving estimation closer to the mandate level recovers downward-sloping estimates without sign restrictions, and price elasticity 2–4 times larger than in consolidated 13F implementations.
Portfolio Granularity and Structural Inference
I show that the statistical power of an observed portfolio does not scale with its raw number of holdings, but with its concentration structure. I study how this property can be used for structural inference with portfolio data.