Use Crash Regressions to Compare Stocks for Option-Based Crash Hedges
Summary
The document describes a screening method for finding stocks whose behavior during sharp market declines differs from their average relationship with the broad market. It aligns daily stock and SPY returns, estimates each stock’s market beta over the full sample, then repeats the regression on days when SPY falls beyond a stated threshold. Subtracting the full-sample beta from the crash-period beta produces a measure called convexity, which is used to rank stocks.
The motivation is to identify candidates for crash protection using options, with put options mentioned as a related hedging approach. The document illustrates return comparisons and outlines the regression workflow, but gives no ranked results, option prices, hedge construction, or performance evidence. Its crash-period estimate depends on the chosen threshold and available observations; the text also notes that whether to include an intercept is debatable. The screening measure alone does not establish that options are cheap or that a resulting hedge will work.
Key ideas
- Compare each stock’s return sensitivity to SPY across the full sample and during large SPY declines.
- Align daily stock and market returns before estimating the regressions.
- Define convexity as the crash-period beta minus the full-sample beta, then rank stocks by that measure.
- The method is a candidate-screening step for option hedging, not proof that options are inexpensive or effective.
- Results may depend on the crash threshold, sample, and intercept choice.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.