What factors influence the likelihood of exceptionally high performance in the hedge fund industry? An extreme value approach of the hedge fund right tail
Schillings, François
Promoteur(s) : Hambuckers, Julien
Date de soutenance : 2-sep-2024/7-sep-2024 • URL permanente : http://hdl.handle.net/2268.2/21293
Détails
Titre : | What factors influence the likelihood of exceptionally high performance in the hedge fund industry? An extreme value approach of the hedge fund right tail |
Auteur : | Schillings, François |
Date de soutenance : | 2-sep-2024/7-sep-2024 |
Promoteur(s) : | Hambuckers, Julien |
Membre(s) du jury : | Hübner, Philippe |
Langue : | Anglais |
Mots-clés : | [fr] Hedge Funds [fr] Extreme Value Theory [fr] Right tail [fr] Penalized regression |
Discipline(s) : | Sciences économiques & de gestion > Finance |
Institution(s) : | Université de Liège, Liège, Belgique |
Diplôme : | Master en ingénieur de gestion, à finalité spécialisée en Financial Engineering |
Faculté : | Mémoires de la HEC-Ecole de gestion de l'Université de Liège |
Résumé
[en] This study investigates the factors influencing exceptionally high performance in hedge funds using an extreme value approach. Hedge funds are noted for their complex strategies and high risk-reward profiles, but research has often overlooked the right tail of their performance distribution, specifically extreme positive returns. By applying Extreme Value Theory (EVT) to a carefully curated dataset of hedge fund characteristics and external economic variables, this research identifies conditions that increase the likelihood of abnormal profits. The findings offer practical insights for hedge fund managers to refine investment strategies and contribute to the development of more accurate predictive models for extreme returns. The study addresses a notable gap in existing literature by focusing on extreme positive outcomes rather than general performance. It concludes with actionable recommendations for managers and suggestions for future research to enhance predictive models and better understand the dynamics of extreme returns.
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