Date: Wednesday, 3 July 2024, at 12:30pm (Paris) and 6:30pm (Singapore)

Dual format: ESSEC Paris La Défense (CNIT), Room TBA
and via Zoom, please click here

Extreme value regression offers a convenient framework to assess the effect of market variables on hedge funds tail risks. However, its major limitation lies in the need to select a threshold below which data are discarded, leading to significant estimation inefficiencies. In this paper, our main contribution consists in introducing a method to estimate simultaneously the tail and the threshold parameters from the entire sample, improving estimation efficiency. To do so, we extend the tail regression model to non-tail observations with an auxiliary splicing density, enabling the threshold to be internally determined. We then apply an artificial censoring mechanism to decrease specification issues at the estimation stage. Empirically, we investigate the determinants of hedge funds tail risks over time, and find a significant link with liquidity indicators. Sorting funds along exposure to our tail risk measure discriminates between high and low alpha funds, supporting the existence of a fear premium. This is joint work with M. Kratz and A. Usseglio-Carleve.

Kind regards,
Jeremy Heng, Olga Klopp, Roberto Reno, and Marie Kratz
https://crear.essec.edu/crear-events/working-group-on-risk/meetings-2023-2024
and Riada Djebbar (Singapore Actuarial Society - ERM)