Adjusting Asynchronous Market Data Across Time Zones
Summary
The document explains how different exchange hours can make daily returns across regions appear misaligned. News arriving after one market closes may be reflected in another market’s same-day return but only in the first market’s next session. Comparing these observations directly can distort relative-price signals and backtests, especially for short-horizon strategies.
Suggested approaches include converting timestamps to a common time zone, estimating an average cross-market adjustment with a moving-average model, and aggregating to weekly observations. The answers characterize the moving-average adjustment as an average correction and caution that it may not capture occasional large moves; they suggest estimating it over a reasonably long span. Weekly aggregation can reduce timing mismatch but may be unsuitable for signals that decay over a few days. The document offers discussion and references rather than a comparative empirical test, and it leaves open whether adjusted observations should also be used for statistics such as volatility.
Key ideas
- Different closing times can shift the same market event into different daily return periods.
- Directly comparing asynchronous prices can bias relative-value signals and backtests.
- Timestamp conversion to a common time zone clarifies when observations were recorded.
- A moving-average model can estimate an average adjustment between markets, but it may miss concentrated large moves.
- Weekly aggregation reduces timing mismatch at the cost of detail needed by short-horizon signals.
Tags
Full text
# How to account for market movement when some exchanges are closed? # How to account for market movement when some exchanges are closed? Daily data, such as open and close prices, is often available for much longer periods than high-frequency data. However, whenever backtesting any strategy that examines instruments traded in different time zones and at different exchanges, one faces the problem of how to account for market movement while one exchange was open and the other closed. In other words, the open and close prices recorded by the exchanges and reported by most data providers do not line up. Thus if major market-moving news comes out during the US day time but after Asian or European markets have closed, then the impact of that news will be reflected in the same day's returns, but won't be reflected in Asian/European markets' returns until the following day. Consequently, any signals generated simultaneously from both markets' returns and/or open/close prices will be biased. For example, suppose US markets rally strongly going into the close. Then US markets may appear overvalued relative to European and Asian markets judging by index prices as of the US close, although in reality the latter markets have moved as well (as ETFs traded on US exchanges would clearly indicate). Since no trading has occurred (yet) in non-US markets, index prices are not representative of true market prices. What methods should one use to reconcile the open and close prices to be on a similar timescale? Would it be better to simply ignore the much longer history which has daily data but no high-frequency data? If you choose to interpolate, should you use the interpolated points to calculate other statistics, such as volatilities, as well? Suppose one is dealing with a relatively short-horizon signal (decaying over 5-10 days), so that smoothing out these differences by looking at longer return periods (e.g. weekly) is not feasible. ## Answer by Patrick Burns (score 5) https://quant.stackexchange.com/a/2627 The blog post http://www.portfolioprobe.com/2011/11/21/asynchrony-in-market-data/ explains a bit more about the problem and it also points to a paper that shows that a moving average model is the way to make the adjustment that Tal is seeking. The paper is presented in the context of a multivariate garch model. That is gratuitous, really -- the MA estimate is going to be just about the same whether or not garch effects are taken into account. I would think that getting one MA estimate for each pair of markets would be sufficient (and probably better) than an estimate for each pair of assets. Caveat: the adjustment gives you an average. So even if the MA estimate were perfect, it would still only give you the adjustment on average rather than (perhaps) a few big moves and lots of basically zero moves. So I think you would want to do this for a reasonably long timespan if you do it at all. ## Answer by wburzyns (score 2) https://quant.stackexchange.com/a/2619 You need to know timezone for each instrument. Then for each instrument convert its time data from local to UTC (or to any other timezone that is convenient to you). Implementation hint: use the freely available Olson database. Timezone conversion routines are easy to find for every serious programming language. ## Answer by pangyuteng (score 0) https://quant.stackexchange.com/a/2618 Probably not the best solution, but an alternative approach would be to look at the data weekly, e.g. weekly open and close, hence eliminating the 'time zone bias'. -- Here is a plot of varying markets over a week, just for fun... ## Answer by Nick (score 0) https://quant.stackexchange.com/a/67945 In the study, the random walk assumption on stock prices (Burns et al. 1998) was relaxed and generalisation of synchronisation model was proposed: the unrecorded returns for the earlier markets make up a fraction of the next day’s asynchronous returns.
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