Reference : Smooth-transition regression models for non-stationary extremes
Scientific journals : Article
Business & economic sciences : Quantitative methods in economics & management
http://hdl.handle.net/2268/255301
Smooth-transition regression models for non-stationary extremes
English
Hambuckers, Julien mailto [Université de Liège - ULiège > HEC Liège : UER > UER Finance et Droit : Finance de Marché >]
Kneib, Thomas [University of Goettingen > Economics department > Chair of Statistics > >]
2021
Journal of Financial Econometrics
Oxford University Press
Yes (verified by ORBi)
International
1479-8409
United Kingdom
[en] Extreme value theory ; generalized Pareto distribution ; operational risk ; VIX
[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
Asset and Risk Management (HEC Recherche)
Banque Nationale de Belgique = National Bank of Belgium - BNB ; Deutsche Forschungsgemeinschaft - DFG
REFEX
http://hdl.handle.net/2268/255301

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