This is a thoughtful framework, especially the distinction between debt acting as a constraint rather than a direct trigger. That resonates, particularly the idea that higher debt levels create an environment with less margin for error rather than an immediate catalyst for market stress.

I’m curious about the indicator itself and how you distinguish moments when risk is being mispriced versus periods where volatility is simply low. In practice, what are the observable inputs that tell you risk is under- or over-compensated in the present?

A few points I’d love more clarity on: is the indicator primarily driven by volatility measures like implied versus realized volatility, term structure, or skew, or does it also incorporate cross-market signals such as rates, credit, or FX relationships? How does it behave during regime transitions, when volatility shifts abruptly rather than gradually? And since you mention it functioning across both rising and falling rate environments, what specifically tells you that conditions have changed enough to warrant adjusting exposure?

I really appreciate the emphasis on flexibility over long-duration conviction. Understanding how that flexibility is actually operationalized would be incredibly helpful.

Well put! Praise Mises

Excellent piece and very insightful. It raises an important question I keep coming back to: how does one truly manage risk in a market where nearly every company now has AI exposure? Traditional diversification feels less effective when the same theme drives sentiment across sectors. With valuations already reflecting ambitious growth assumptions, I’m curious how others are approaching portfolio construction and downside protection in this environment.