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Causal Inference for Asset Pricing
(with Valentin Haddad, Zhiguo He, Paul Huebner, and Peter Kondor)

First version: Fall 2024
This version:  Winter 2025


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Abstract:

This paper provides a guide for using causal inference with asset prices and quantities.
Our framework revolves around two simple assumptions: homogenous substitution conditional on
observables and constant relative elasticity. Under these assumptions, standard cross-sectional
instrumental variable or difference-in-difference regressions identify the relative demand
elasticity between assets, the difference between own-price and cross-price elasticity.
In contrast, identifying aggregate elasticities and substitution along specific characteristics
necessarily relies jointly on exogenous sources of time-series variation alone.
The same principles apply to the estimation of multipliers measuring the price impact of supply or
demand shocks. The two assumptions map to familiar restrictions on covariance matrices in
classical asset pricing models, encompass models from the industrial organization literature
such as logit, and accommodate rich substitution patterns even outside of these models.
We discuss how to design experiments satisfying these conditions and offer diagnostics to validate
them.


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