Industry Voice: When the Dust Flies - How Volatility Events Affect Asset Class Performance

clock • 3 min read

We investigate asset class performance before, during and after sharp increases in equity market volatility, and examine specifically how stocks and bonds perform during such events and whether asset class performance after such an event is different than leading into the event.

We define two types of volatility events: "spikes" and "post peaks." A volatility spike event is a significant, sudden increase in volatility, and we measure asset class performance while "the dust is flying." In contrast, a volatility post-peak event is a period of high volatility that is followed by volatility returning to its pre-peak level, and we measure asset class performance "after the dust has settled."

History shows that volatility spike events produce large negative returns for equities and credit bonds (with positive returns for Treasuries) which may be unnerving to some investment committees.  However, markets recover relatively quickly, approximately by the seventh month following the volatility spike month.

Some investors may wish to wait until the "dust settles" - either out of caution or time needed for deliberation - before re-examining their investment strategy. Once volatility has smoothed out, equity and credit markets still tend to perform well.

The evidence from both types of volatility events suggests that the damage from those volatility events is transitory and is likely to be repaired after a reasonable period, providing support for investment committees who intend to "stay the course," and possibly re-balance with increased allocations to risky assets.


For Professional Investors only. All investments involve risk, including the possible loss of capital.

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All investments involve risk, including the possible loss of capital. There is no guarantee that any particular asset allocation will meet your investment objectives. The findings shown herein are derived from statistical models. Reasonable people may disagree about the appropriate model and assumptions. There can be no assurance that model returns can be achieved.

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