Why Drawdown Granularity Matters in Long-Term Strategy Studies
Summary
This discussion asks whether academic papers and industry white papers that present monthly strategy results calculate drawdowns from daily data or only from month-end observations. The distinction matters because a monthly sample can miss losses and peaks that occur within a month, making reported maximum drawdown appear smaller. The author is especially interested in long historical studies, where daily total-return data may be difficult to collect consistently, and wants to know what researchers actually do for older periods.
A response says drawdowns can be measured at the finest available data frequency and gives the May 2010 equity-market disruption as an example of an intramonth event that monthly observations could obscure. It contrasts drawdowns with statistics such as Sharpe ratio and value at risk, for which high-frequency estimates may be difficult to interpret. The exchange does not identify specific papers or establish how common daily calculations are, so it raises the reporting question without resolving it.
Key ideas
- Monthly observations can miss intramonth peaks and losses, understating measured drawdowns.
- Historical daily total-return data may be difficult to assemble consistently.
- A response recommends using the finest available data frequency for drawdown calculations.
- The discussion does not establish what frequency is standard in published long-term studies.
- High-resolution Sharpe ratios and value-at-risk estimates may be difficult to interpret.
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Full text
# Are Papers and Funds reporting Monthly drawdowns using daily granularity? # Are Papers and Funds reporting Monthly drawdowns using daily granularity? I'm curious as to how many academic studies and industry white papers are actually using daily data to report intramonth drawdowns; specifically, when the papers are often reporting monthly signals, statistics, and performance. I would think it would be obvious that honest reporting would report risk statistics using daily data granularity, but considering they do not often explicitly state such, it is hard to say with certainty. Many of the daily data series themselves are hard to gather and guarantee same results (dividends, etc) over the long term. But there are numerous papers on topics such as multi-asset momentum strategies going back to the seventies. There would be a large difference in some risk metrics like drawdown if only monthly closing data points were sampled. Any first-hand experience or references on the matter are appreciated. edit: Thanks for replies so far. Just for clarification; I'm not really asking about the merits or pitfalls of sampling at different intervals-- I'm well aware of that. I'm asking more about experiences with various papers(academic) and white-papers(industry) that show monthly statistics back to the seventies (or more) and whether or not you've found that they divulge risk metrics (esp. drawdowns) based on daily or only monthly granularity. It's important for comparison purposes to understand if they are underestimating risk measures in such old data. If some paper, displaying only monthly results, charts, and tables, tells me that the worst drawdown over 40 + years was -25% (some use data going back to twenties), I want to know if that included daily granularity or not. Unfortunately, I don't often see that clarification and so I'm wondering if it is the norm to only use monthly sampling for long term systematic studies with potentially sparse daily data available on total return series. There are some high low data available from CRSP and IDSI going back to the 60s, so I agree with Freddy that it can be done, just more interested in what has actually been applied in papers with older data, so they can be compared reliably. ## Answer by elleciel (score -2) https://quant.stackexchange.com/a/7098 You're right to recognize that the sampling interval of your risk metrics could make a huge difference. For drawdown, there's no reason not to use the same granularity as the data that one has, unless the author is deliberately shaping the curve to appear better than it actually is. A trivial example of this would be if you had a buy-and-hold portfolio in cash equities through May 6, 2010. However, for Sharpe ratio, VaR etc., it is difficult to get a meaningful value at high resolution.
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