Extreme value theory; generalized Pareto distribution; operational risk; VIX
Abstract :
[en] We introduce a smooth-transition generalized Pareto (GP) regression model to study the time-varying dependence structure between extreme losses and a set of economic factors. In this model, the distribution of the loss size is approximated by a GP distribution, and its parameters are related to explanatory variables through regression functions, which themselves depend on a time-varying predictor of structural changes. We use this approach to study the dynamics in the monthly severity distribution of operational losses at a major European bank. Using the VIX as a transition
variable, our analysis reveals that when the uncertainty is high, a high number of losses in a recent
past is indicative of less extreme losses in the future, consistent with a self-inhibition hypothesis.
On the contrary, in times of low uncertainty, only the growth rate of the economy seems to be a
relevant predictor of the likelihood of extreme losses
Research Center/Unit :
Asset and Risk Management (HEC Recherche)
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Hambuckers, Julien ; Université de Liège - ULiège > HEC Liège : UER > UER Finance et Droit : Finance de Marché
Kneib, Thomas; University of Goettingen > Economics department > Chair of Statistics
Language :
English
Title :
Smooth-transition regression models for non-stationary extremes
Publication date :
2023
Journal title :
Journal of Financial Econometrics
ISSN :
1479-8409
Publisher :
Oxford University Press, United Kingdom
Volume :
21
Issue :
2
Pages :
445-484
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
REFEX
Funders :
BNB - Banque Nationale de Belgique [BE] DFG - Deutsche Forschungsgemeinschaft [DE]