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Consider the following problem:

  • You would like to understand how a particular Multi Asset Class fund is invested (ascertain the weights attributed to each asset class)
  • You have at your disposal a series of daily returns for that fund
  • You also have daily returns for broad asset classes (Gov Bond index, local equities, foreign equities, High Yield, ...)
  • Crucially, you don't know in which of those asset classes the fund is invested in (could be only a subset of these or worse, could be in an asset class outside the set)

How would I go about determining how the fund has been invested: - On average over the entire period - On a rolling window basis

And how might I determine if the fund is invested in assets not in the above set?

My thoughts on the matter:

  • I could use rolling regressions on the daily returns, excluding an intercept term (the CASH asset class would act like my constant anyway).
  • From that regression, the coefficients would be my weights and the error term would provide clues as to whether the model specification is lacking any asset classes
  • An issue with this approach might be the unreliability of the coefficients if two or more asset classes are highly cointegrated (eg: Small and large cap equities or AAA and AA rated bonds)

I've looked far and wide, but I haven't been able to find a single source that looks at this particular problem. If someone can point me in the right direction, I would be very appreciative.

Thank you for your help and insight!

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You're on the right track. Use Multiple Regression, see also return based style analysis
If you are concerned with time series being to correlated you could also use Principal Component Analysis (PCA).

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