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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 ...


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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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