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There is never enough market data for testing. And sampling from user defined distribution is a hotly debated subject as which distribution does the market really go with?

There are many ways to generate synthetic data series for sensitivity testing but the methods should be sound. Does the follow method warrant further investigation? Extract daily returns from n different instruments. Mix and match them from sampling with replacement. After each new series, mix them again before generating another one. To avoid major differences in the underlining characteristics, the pool of return should come from one asset class. For example, synthetic data from stocks should come from the pool of equities.

Does this make sense? Or is it too naive?

Thanks,

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    $\begingroup$ You will lose the time series characteristics. Even if log-returns are uncorrelated - their absolute value or their square often is not. Sampling in this way you will lose this feature $\endgroup$ – Ric Oct 4 '12 at 12:37
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You can look into block bootstrapping as one alternative to mitigate loss of any serial dependency effects.

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