  We introduce a method to estimate simultaneously the tail and the threshold
parameters of an extreme value regression model. This standard model finds its
use in finance to assess the effect of market variables on extreme loss
distributions of investment vehicles such as hedge funds. However, a major
limitation is the need to select ex ante a threshold below which data are
discarded, leading to estimation inefficiencies. To solve these issues, we
extend the tail regression model to non-tail observations with an auxiliary
splicing density, enabling the threshold to be selected automatically. We then
apply an artificial censoring mechanism of the likelihood contributions in the
bulk of the data to decrease specification issues at the estimation stage. We
illustrate the superiority of our approach for inference over classical
peaks-over-threshold methods in a simulation study. Empirically, we investigate
the determinants of hedge fund tail risks over time, using pooled returns of
1,484 hedge funds. We find a significant link between tail risks and factors
such as equity momentum, financial stability index, and credit spreads.
Moreover, sorting funds along exposure to our tail risk measure discriminates
between high and low alpha funds, supporting the existence of a fear premium.
