BSc Banking and Finance
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Bachelorarbeit – Banking and Finance
Estimating Multi-Beta Pricing Models With or Without an Intercept. Further Results From Simulations GRADUATE Florin Akermann SUPERVISOR Armin Bänziger-Aiba
The two-pass method is a common approach for estimat
ases of these estimates are already small without omitting
ing risk premiums and examining factor pricing models. It
the intercept in either of the two regressions. Moreover, the
consists of a time series regression (first-pass) and a cross-
standard errors of the estimates for the size and value pre
sectional regression (second-pass). Two common prob
miums did not decrease when the intercept was omitted in
lems of this approach are a downward bias and the large
at least one of the regressions. In all of the applied variants,
standard error of estimates. A previous study using a simu
the standard error of the estimates for the three premiums
lation approach showed that the problem of the bias could
was consistently large. Therefore, even with this partially
be mitigated by running at least one of the two regressions
effective mitigation method, it remains difficult to draw sta
without an intercept, while the problem of the large stan
tistical conclusions from the two-pass method.
dard error can be mitigated by running the second regres sion without an intercept. The study mentioned above used a single-factor pricing model as the underlying model for its simulation. The ob jective of this bachelor’s thesis was to provide further evi dence for this mitigation method (leaving out the intercepts) by analyzing the mitigating effects in the case of the Fama and French three-factor model. For this purpose, the simu lation was based on the simulation of the previous study, which was extended to suit the properties of the threefactor model. The simulation consisted of two main parts: First, the test data was generated artificially, then the twopass method was applied to each set of this generated data. Similar to the findings of the underlying study, it was found that omitting the intercept in at least one of the two regres sions decreases the bias of the estimated market premi um. Furthermore, omitting the intercept in the cross-sec tional regression decreases the standard deviation of the market premium estimates. However, for the two risk pre miums (size and value) estimated additionally, the mitigat ing effect on the biases was barely observable as the bi