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.
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).