How to Estimate Drawdown Recovery Time
A drawdown's depth gets all the attention, but its duration is what tests an investor's resolve. A strategy that recovers in two months is very different from one that takes two years, even at the same depth. Recovery time depends on the return distribution, and because returns are skewed and fat-tailed, the recovery is a distribution with a long tail rather than a point. How to simulate that distribution honestly and how to read the output are both covered below.
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Before You Start
Set up the inputs that make the next steps easier
Guide Steps
Move through it in order
Each step focuses on one decision so you can keep momentum without losing the thread.
- 1
Estimate the return moments
Gather the four moments of the monthly return distribution: mean, volatility, skewness, and excess kurtosis. The mean and volatility drive the central tendency of recovery, but skew and kurtosis shape the tail, which is where the painful long recoveries live. Estimate these from out-of-sample returns where possible, since in-sample moments inherit the optimism of the fit and will understate how bad recovery can get.
Use enough history to estimate the higher moments stably. Skew and kurtosis are noisy in short samples, and they are exactly the inputs that govern the tail of recovery.
Use The ToolCalculatorsReturns Distribution Analyzer
Paste a returns CSV. Histogram, normal-overlay, QQ plot, skewness, excess kurtosis, Jarque-Bera test, tail-weight index. See why Sharpe alone misleads.
ToolOpen -> - 2
Simulate paths with Cornish-Fisher draws
Generate many simulated forward return paths drawing from a distribution that respects the skew and kurtosis you estimated, rather than a normal distribution. A Cornish-Fisher expansion adjusts normal draws for the third and fourth moments, producing draws with the asymmetry and fat tails of real returns. Simulating from a normal distribution understates recovery time because it ignores the fat-tailed sequences of losses that stretch recoveries out.
Do not simulate from a normal distribution for a fat-tailed strategy. It will give a falsely reassuring recovery estimate by ignoring the bad-streak sequences that actually trap capital.
Use The ToolCalculatorsDrawdown-Recovery Markov Simulator
Time to recover from an N% drawdown given monthly Sharpe + skew + kurtosis. Cornish-Fisher Monte Carlo, percentile distribution of recovery months.
ToolOpen -> - 3
Measure recovery to the prior peak
Starting from the drawdown depth you care about, count how many months each simulated path takes to climb back to the prior high-water mark. Some paths recover quickly; others compound losses and take far longer. The collection of recovery times across all paths is the distribution you want, capturing not just the typical case but the unlucky sequences that define how bad it can realistically get.
Recovery is measured to the prior peak, not to break-even on the next trade. Investors feel the drawdown until the old high is reclaimed, and that is the duration that matters.
- 4
Read the percentile distribution
Report recovery time as percentiles, not a single average: the median, the 90th percentile, and the 95th or 99th. The tail percentiles are what matter for planning, because they represent the bad-but-plausible scenarios that test whether you can hold the strategy. A median recovery of four months with a 95th percentile of two years is a very different commitment than the median alone suggests, and the tail is the honest number to plan around.
Plan around the 90th or 95th percentile recovery, not the median. The median is the story you tell when things go to plan; the tail is the one that decides whether you capitulate.
- 5
Use the result to size and to set expectations
Feed the recovery distribution back into your sizing and your psychological preparation. If the tail recovery exceeds what you can sit through without abandoning the strategy, reduce position size until it does not, since smaller positions shorten recovery. Equally, knowing the realistic tail in advance is what lets an investor hold through a long drawdown rather than capitulating at the bottom, which is the most expensive mistake in the recovery.
The point of estimating recovery is to not be surprised by it. An investor who expected a long drawdown holds; one who did not capitulates at the worst time.
Common Mistakes
The misses that undo good inputs
Simulating recovery from a normal distribution
Real returns are skewed and fat-tailed. A normal simulation ignores the bad-streak sequences that stretch recoveries out, giving a falsely short and reassuring estimate of how long recovery takes.
Reporting only the average recovery time
Recovery time has a long right tail, so the average hides the bad-but-plausible scenarios. Planning around the mean leaves you unprepared for the tail recoveries that actually test whether you can hold.
Using in-sample moments
Moments estimated from the data the strategy was fit on inherit the optimism of the fit, understating volatility and the tails. The recovery estimate built on them is too optimistic, exactly where realism matters most.
Try These Tools
Run the numbers next
Risk-Adjusted Returns Calculator
Paste a returns CSV. Sharpe, Sortino, Calmar, Omega, alpha, beta, tracking error, information ratio, max drawdown, and tail moments — plus.
Fractional Kelly Sizer
Map conviction tiers to fractional Kelly bet sizes with a drawdown Monte Carlo simulator. Client-side. Private by default.
FAQ
Questions people ask next
The short answers readers usually want after the first pass.
Sources & References
- The Cornish-Fisher Expansion in the Context of Delta-Gamma Normal Approximations — Cornish and Fisher (1938), applied finance reference
- The Statistics of Sharpe Ratios — Andrew W. Lo, Financial Analysts Journal (2002)
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