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In the credit modelling industry is more popular the use of the logistic regression with respect to the Poisson one. This is for several reasons. Here I listed the main ones: 1) The Logistic regression is empirically shown to be better in describing that kind of phaenomenona in terms of forecasting performances and predictive capacity (try to compare the ...


In the second pass, the independent variables are the first pass estimated betas. That is, you estimate $\hat{\beta_i}$ in time series for every stock i $$r_{i,t} - r_{f,t} = \alpha_i + \beta_i(r_{M,t}-r_{f,t}) + \epsilon_t$$ and then you estimate risk premia $\hat{\lambda}$ according to the following regression: $$\overline{r_{i,t} - r_{f,t}} = a_0 + ...

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