Are extreme 1-in-100 events becoming normal, and are current models fundamentally broken?

Most institutional investors have had the same quiet moment over the past two decades. It usually arrives in an investment committee meeting, while reviewing a portfolio or watching a Bloomberg screen as an asset class makes a move that was supposed to be exceptionally rare. It is less a moment of surprise than of uneasy familiarity, as another event once considered extraordinary joins an increasingly familiar list.

Among the most consequential market dislocations of the past 20 years have been the global credit crisis, the pandemic liquidity freeze, the fastest hiking cycle in a generation, regional banking stress and renewed global trade conflict. Put them together and it is reasonable to ask whether something in the financial system has changed, and whether events that were supposed to be rare have become ordinary.

That was the question I set out to explore through conversations with David Rosenberg, founder of Rosenberg Research, and Corrado Tiralongo, chief investment officer, Canada Life Investment Management.

Rather than debating whether extreme events are becoming more frequent, both practitioners argue that the underlying behavioural drivers of markets remain largely unchanged, even as market structure, information flow and liquidity conditions continue to evolve. Their focus is not on predicting the next crisis, but on building portfolios that continue to function when predictions inevitably fall short.

Are shocks actually more frequent?

Looking at the speed and publicity of recent drawdowns, it is easy to conclude that the old rules no longer apply. However, Rosenberg and Tiralongo see the forces at work as familiar.

Rosenberg reads the current environment through the lens of the long economic cycle, which runs through the macro framework behind his Rosie Model Portfolio and which he believes is in its later stages. Expansion, leverage, contraction, repeat. Someone who has watched that sequence run several times tends to be unimpressed by the suggestion that it has stopped running.

Rosenberg says, “There is no such thing as a new era.” Cycles change shape and duration, but not their underlying character. A cycle that takes longer to turn gives an economy more time to accumulate excesses. Those prolonged imbalances make the eventual correction look extreme, even though the driver behind it remains entirely ordinary. That deep respect for mean reversion leaves him systematically wary of any overextended market where risk appears to cost nothing.

Tiralongo explores behaviour: how investors respond to uncertainty, how confidence builds and how narratives emerge to justify increasingly crowded positioning. Fear, greed and complacency have not changed. Because the future is unpredictable, he focuses on execution over forecasting: “It’s not about trying to predict when that crisis is going to occur. It’s really structuring the portfolios and the frameworks to be functional when a crisis occurs or when your forecast is wrong.”

Both reference instantaneous news cycles. What has changed, both suggest, is the speed at which information, markets and policy interact. News now travels almost instantly. Capital reprices within minutes or hours rather than days, and policymakers typically respond more quickly than they once did. Imbalances still build slowly, often through long periods of calm. It is the unwind that has compressed. Rosenberg goes further, suggesting the baseline frequency of severe shocks over multi-decade windows may actually be lower than it once was. The underlying probability may not have changed much, but the speed and intensity with which those shocks unfold make them feel more frequent and more memorable.

Designing for uncertainty

For both practitioners, the answer is not to improve forecasting, but to change how portfolios are built. Institutional frameworks have long used historical data to price risk and set allocations, assuming the correlations observed during calm periods would still hold in a crisis. Tiralongo notes the limitation is structural: “Conventional analytics describe the outcome. Decision analytics connect the choices made to the results those choices produced.”

Assets that appear uncorrelated in quiet markets can move together once liquidity tightens, particularly when investors are forced to raise cash simultaneously. Rosenberg and Tiralongo handles this by shifting away from more elaborate models or attempting to name the next negative catalyst.

They still make forecasts, but the more useful question is whether the portfolio remains functional when those forecasts are wrong. Uncertainty is expected. The practical task is to build something that keeps working even after you miss the mark

Where this shows up

This shift in perspective directly alters a few practical areas of portfolio construction.

Inside the committee room

The habit of treating every disruption as a historical anomaly may miss the point. Whether the world is producing more frequent shocks is a question for academics, not practitioners.

Go back to that quiet moment in the committee room, the one that seems to arrive every few years. What matters in that instance is whether the portfolio can absorb the shock and whether the people around the table know exactly what they are permitted to do.
The real evolution in portfolio management stems from accepting the limits of prediction. As Rosenberg puts it, “You always want to have an insurance policy against being wrong.” Modern asset allocation is shifting toward building frameworks resilient enough to survive when those forecasts inevitably prove incorrect.

Written by

Sebastien Davies, CFA, is a partner at Primal Capital, investing across technology, financial infrastructure and digital assets. He has a background in institutional capital markets and emerging financial technologies and he writes about markets, investing and how technology is reshaping financial systems, with an emphasis on real-world application rather than theory.