Adjusting Portfolio Risk for Asynchronous Cross-Market Data
Summary
Market closes occur at different times across regions, so daily returns for highly related assets may reflect different information windows. This timing mismatch can depress measured correlations between markets, such as Japan and the United States, and distort portfolio risk estimates.
The answer describes an approach that accounts for lag-one cross-correlations when estimating volatility and risk contributions over holding periods longer than one day. It says the resulting estimates are often similar to those obtained with weekly data, where closing-time differences have a smaller effect. Using daily data with the timing effect modeled may be more responsive and provide more observations for risk analysis and portfolio optimization. Simply shifting series by a full day is not presented as a general fix; such shifts may be appropriate for stale fund NAVs that refer to an earlier market date. The document offers practitioner guidance rather than a detailed derivation or empirical comparison, and the cited method is not explained step by step.
Key ideas
- Different market closing times can lower measured daily correlations between related assets.
- Timing mismatches may appear as increased lag-one cross-correlations.
- Risk estimates can account for these lagged relationships, particularly for multi-day holding periods.
- Weekly data can produce similar estimates, though daily data may offer more observations and responsiveness.
- Shifting a series by a full day is not a general solution, though it can address stale fund NAV dates.
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Full text
# Asynchronous Data Across Time Zones - RiskMetrics # Asynchronous Data Across Time Zones - RiskMetrics I'm currently involved with a project to integrate RiskMetrics into our business and one issue we've identified is the treatment of market data timing across time zones. This can have the effect of lowering correlations between securities we can otherwise observe being highly correlated. We've tried a couple of approaches to deal with the issue but none have been satisfactory in our opinion. Just wondering if anyone else has dealt with this issue and what solutions have been proposed. We have gotten to the point of considering uploading our own data series, with all the complications that come with that. ## Answer by Richi Wa (score 2) https://quant.stackexchange.com/a/9046 as vanguard2k points out the prolem is dealt with e.g. in Scaling portfolio volatility and calculating risk contributions in the presence of serial cross-correlations and references therein. It turns out that correlations are lowered while lag one cross-correlations increase. E.g. you can probably see a correlation of Japan today to US yesterday due to the different closing times. The approach desribed above tries to incorporate this effect in volatility estmates for holding periods of more than one day. This approach usually yields similar estimates as using weekly data (where closing-time differences still play a role but a much smaller one). But in my mind using daily data (and handling the effects described above) is superior in ex-ante risk analysis and portfolio optimization (more reactive, more data points). In my experience you can not solve this problem by shifting data by full days. Shifting by full days however can be necessary treating funds whose pubslished NAV corresponds to a market date in the past (T-1 e.g).
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.