Handling Asynchronous Market Closes in Multi-Asset Data
Summary
The document considers daily price series for assets traded in markets with different opening and closing times. Such noncontemporaneous observations can create lagged cross-correlations—for example, one market's current return may align with another market's prior-day return. The cited discussion says a moving-average time-series process can represent this lag structure, which may matter in risk estimation and trend fitting.
Suggested approaches include aggregating observations to weekly frequency, constructing synchronized returns, using state-space or Kalman filter methods to handle unavailable observations, and applying the Hayashi–Yoshida estimator for asynchronous covariance. The appropriate method depends on whether the goal is trend estimation, synchronized price construction, or covariance measurement. The answers provide references and broad options rather than a comparative empirical test; they also caution that more elaborate missing-data techniques may be unnecessary and that results should be checked for plausibility.
Key ideas
- Different market closing times can produce lagged cross-correlations in daily asset returns.
- A moving-average process can model a one-step lag structure caused by asynchronous observations.
- Weekly aggregation or synchronized-return construction can reduce timing mismatches.
- Kalman filters can handle observations arriving at different times or with gaps.
- The Hayashi–Yoshida estimator offers a covariance approach that does not require synchronizing observations first.
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# Data Synchronization # Data Synchronization I'm working on market trends. I have daily prices for 33 assets from different markets. I was wondering if there is a way to cancel the effects of different opening/closing times. I have been told that a moving average over four days would be enough; I think a weekly moving average should improve many thing. As I observe a 50-day moving average to observe market trends, I don't really see the point in doing this first moving average. Is there any literature about this topic? Are there simple solutions to cancel the effects of markets desynchronization? ## Answer by Richi Wa (score 9, accepted) https://quant.stackexchange.com/a/7654 Effects of non-contemporaneous trading (i.e. different closing times) for risk management are covered in this article (preprint,link to journal). The conclusion is that a moving average process in the sense of time-series analysis can handle the resulting cross-autocorrelation. This means that in each time-step you have lagged correlations (e.g. Japan today to US yesterday) but only for lag $1$. In case you want to use e.g. the Hodrick-Prescott-filter in a Kalman filter setting for fitting a trend, this model approach could improve the picture (I can not tell from experience but as an idea ...). For hints on the HP-filter you can start here. ## Answer by John (score 3) https://quant.stackexchange.com/a/7655 Another solution is to convert the daily series to weekly. Alternately, if you have to use the daily data, this paper describes how to construct a synchronized return, which you could presumably adjust it into a synchronized hypothetical price. In general, the issue is one of missing data, which can quickly get you into some advanced techniques (e.g. generating the missing data in a Gibbs sampling approach). The Kalman Filter approach that Richard mentioned also is popular in the literature. In economics, they use Kalman filters to "Nowcast" in order to handle different publication lags, which is similar to some data not being available from some markets. I would take extra care that the results makes sense. ## Answer by Quartz (score 2) https://quant.stackexchange.com/a/8209 If you're asking for the covariance structure, then the simplest asynchronous estimator is that from Hayashi-Yoshida - Corsi-Audrino which avoids any bias from synchronization. There is a wide literature developed on top, from dealing with microstructure noise&autocorrelation to improving efficiency, but the basic form should usually suffice in practice. VMA&c and filters are surely also feasible, but sometimes overkill, just like classical missing data methods such as expectation maximization.
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