3
$\begingroup$

I want to measure the positioning of an active bond mutual fund vs. its benchmark via rolling linear regression of returns vs several factors. The intuition of using linear regression is that the returns of the index or of the fund ($r$) should be a linear function of the returns of several sub indices (i.e. rates $r_{\text{10yr}}$, credit spreads $r_{\text{cred spreads}}$, etc..).

The regression takes the form: $$r = \beta_1 \times r_{\text{10yr}} + \beta_2 \times r_{\text{cred spreads}} + \cdots$$

The R-squared of the regression of the index vs. the sub-indices is ~95%. Likewise the R-squared of the regression of the mutual fund vs. the sub indices is ~85%.

I want to know whether it is better to measure relative positioning using the difference in the Betas to each factor from the regressions, i.e $\beta_{\text{fund}}$ - $\beta_{\text{index}}$ or if it is better to run a single regression of excess returns of the fund over the benchmark vs. the same set of factors:

$$(r_{\text{fund}}-r_{\text{index}}) = \beta_1 \times r_{\text{10yr}} + \beta_2 \times r_{\text{cred spreads}} + \cdots $$

I believe running separate regressions allows for the error terms to be estimated with different variances but what other factors should I think about? A third possibility is to run a panel regression were sensitivities are estimated simultaneously but the marginal effect is captured through an interaction term for each variable/fund combo.

$\endgroup$
2
$\begingroup$

That's a quite interesting problem, a few thoughts on how to attack it:

  1. Calculate the correlation and beta between the benchmark and the fund.
  2. If the above imply a link between these two then proceed with the betas' comparison.
  3. Regarding the three approaches you mention, the one which subtracts the betas sounds mathematically-speaking wrong since beta is a volatility-based measure.
  4. So I would go either for the panel regression or the one where you apply the regression on (rfund−rindex) - probably for the panel regression.

Hope that helps.

$\endgroup$
  • $\begingroup$ Thanks for your answer. Any elaboration on point 3 would be helpful. $\endgroup$ – Alex Jun 9 '17 at 13:20
  • $\begingroup$ well, it's just an intuition... you would expect of course a timeseries with a higher beta to be more aggressive but how do you measure it? by taking the difference you are subtracting a measure whose computation includes volatilities so it doesn't sound mathematically sound. $\endgroup$ – sen_saven Jun 9 '17 at 14:11

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.