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I try to calculate the cost of equity with the FF3 model and already estimated the beta factors for the market, size and value risk premia by using regressions and the data provided on the Kenneth French website.

Now my question is where I can get the specific risk premia in order to calculate the return? I know that I can use the market risk premium as published by Damodaran but how do I get the other two?

I really appreciate your help!

Kind regards, Felix

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    $\begingroup$ have you tried on CRISP database $\endgroup$ – Vitomir Jul 18 at 12:17
  • $\begingroup$ No. Unfortunately, my university does not provide me access to any commercial database such as CRISP for my thesis... $\endgroup$ – Felix S. Jul 18 at 12:32
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You can always build them from first principals. I have had to do this at funds I worked for in South Africa. The HML and SML zero-investment-portfolios (ZIPs) are pretty well defined.

The first choice is to use the CRISP data set but you will run into the same problem if you try to build these factors on any none US-listed equities. You can download historic data from Bloomberg or TR and then get started. Yes, you will need to do your best to account for all the biases. This is a big task, If you're doing it on your own.

You could do all of this in Excell (I know people do) but I would much rather do it in Python and store the data in a data base.

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This is the whole problem with the FF3 model, and indeed the very reason it was ever concocted in the first place!

CAPM 101 argues that the cost of equity is riskless plus market beta. Except this doesn't work, because there are clear value and size; or value, size, and momentum effects also in operation. In statistical parlance, the null hypothesis that these style factors have a zero risk premium can be trivially disproved.

However, proving these have not been not zero does not tell you what value beyond zero they are, let alone "should" be. The factors simply exist to help correct for the divergence of CAPM v1.0 from observed reality. These style effects can be measured ex-post. However, that offers no intuitive ex-ante explanation for why the size effect should be 0.5%, 1%, 2% or 4% per annum; and likewise for value and momentum.

There's a vague and intuitive logic for why the size and value effects exist, with positive risk premia. Momentum is more difficult here. But nobody - to my knowledge - can sensibly suggest an intuitive economic model to suggest what the "fair value" of any of those incremental risk premia is, or should be.

As such, they've always been crimes against theory. Like oh so many elementary particles in physics, they exist to make the equations continue to work. Except that just means we need them for theory's sake. It doesn't mean we actually understand them ;-)

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