We introduce "Factors on Demand", a modular, multi-asset-class return decomposition framework that extends beyond the standard systematic-plus-idiosyncratic approach. This framework, which rests on the conditional link between flexible bottom-up estimation factor models and flexible top-down attribution factor models, attains higher explanatory power, empirical accuracy and theoretical consistency than standard approaches.
We explore applications stemming from factors on demand:
- The joint use of a statistical model with non-idiosyncratic residual for return estimation and a cross-sectional model for return attribution
- The optimal hedge of a portfolio of options, even when the investment horizon is close to the expiry and thus the securities are heavily non-linear
- The "on demand" feature of FoD to extract a parsimonious set of few dominant attribution factors/hedges that change dynamically with time
- Accommodating in the same platform global and regional models that give rise to the same, consistent risk numbers
- Point-in-time style analysis, as opposed to the standard trailing regression
- Risk attribution to select target portfolios to track the effect of incremental alpha signals on the allocation process