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!


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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