EFFICIENT ESTIMATION IN EXTREME VALUE REGRESSION MODELS OF HEDGE FUND TAIL RISKS

Julien Hambuckers (), Marie Kratz () and Antoine Usseglio-Carleve ()
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Julien Hambuckers: HEC École de Gestion de l'Université de Liège
Marie Kratz: ESSEC Business School, CREAR - Center of Research in Econo-finance and Actuarial sciences on Risk / Centre de Recherche Econo-financière et Actuarielle sur le Risque - ESSEC Business School
Antoine Usseglio-Carleve: AU - Avignon Université

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Abstract: 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.

Keywords: Extreme value theory generalized Pareto regression censored maximum likelihood; Extreme value theory; generalized Pareto regression; censored maximum likelihood (search for similar items in EconPapers)
Date: 2023-04-07
New Economics Papers: this item is included in nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-04090916
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