LQG 08/03/22 – On-Line – Risk under uncertainty and price movement – Anish Shah

Portfolio Risks under Estimation Uncertainty and Price Movement

Seminar by Anish Shah

8th March 2022 – On-Line

When uncertainty suddenly manifests as realised risk any resulting financial distress is likely to be amplified by the inevitable and unhappy comparisons with the comforting risk forecasts produced in the recent past.

The news in early 2022 suggests that there may now be “quite a lot” of uncertainty in markets… as well as risk… so now is an optimal time to consider risk under uncertainty. Anish Shah has long considered this issue and has key insights to be discussed at the seminar with details available to download in the paper “Portfolio Risks under Estimation Uncertainty and Price Movement

Risk decomposition is a standard tool for analyzing investment portfolio risk. The portfolio is divided into notional parts—e.g., individual securities, holdings by sector or region, factor exposures—whose contributions to net risk are estimated and reported. Convention regards the inputs—portfolio weights and covariance—as fixed and known, but portfolio composition changes with price movement and estimates have errors. Since behavior only in the direction of net risk is counted, hedged effects are invisible regardless of size. What if numbers aren’t exact? Hedge instability manifests in proportion to underlying gross (not net) exposure, akin to leverage. For example, in a market-neutral portfolio, market is the largest risk in each side, conventionally uncounted since hedged, and extremely consequential under small deviations. To solve the problem, this paper models weights and parameters as uncertain. Evaluating a portfolio across the range of possibility measures risks better and surfaces latent fragility. No longer point-estimated, contributions are reported with a center and spread. 



Anish Shah

Financial Engineer
Smartleaf Asset Management


Anish Shah has been researching portfolio uncertainty for nearly a decade, spurred by the problem of estimation error in optimization. Currently with the robo-advising firm Smartleaf, he previously spent many years in research at Northfield. He has also worked at ITG and the shuttered ship-tracking hedge fund CargoMetrics. He is a CFA charter holder and earned master’s degrees in operations research from UC Berkeley and applied math from Brown University, where he is writing a PhD from this work.