ML-Based Hedge Ratio Estimation

The simplest hedge ratio is a static OLS beta: regress the position's returns on the hedge instrument's returns over some historical window and use the resulting slope. ML-based hedge ratio estimation replaces that single fixed slope with a model — often a regularized regression, tree ensemble, or a rolling/online estimator — trained to output a hedge ratio that adapts as inputs like recent volatility, liquidity, or the relationship's own stability change.

Why a single static beta can misfire

A relationship estimated once over, say, two years of data assumes the correlation between a stock and its hedge index is stable — but that correlation itself moves with the market regime, and a static beta doesn't know whether today's conditions look like a typical day from the estimation window or something unusual. A learned hedge ratio can take features like recent realized correlation, implied volatility, or sector rotation signals as inputs and output a ratio that shifts accordingly — hedging more aggressively when the relationship looks stable and pulling back the hedge size (or flagging low confidence) when the relationship looks like it's breaking down.

The practical cost is the same as any adaptive model layered into execution: more moving parts to monitor, a hedge ratio that can itself become a source of turnover and transaction costs if it updates too often, and a need to validate that the model's adaptiveness is actually tracking real regime shifts rather than chasing noise.