5 citations · 6 across the 12 of their papers we have counts for
15 papers
A sub-asymptotic model for bivariate threshold exceedances
Mirco Lescart, Anna Kiriliouk, Philippe Naveau
Extreme value theory offers a statistical framework for quantifying the risk of rare events, with the generalized Pareto (GP) distribution providing the canonical limit model for u…
A penalized least squares estimator for extreme-value mixture models
Anas Mourahib, Anna Kiriliouk, Johan Segers
Estimating the parameters of max-stable parametric models poses significant challenges, particularly when some parameters lie on the boundary of the parameter space. This situation…
Tail Risk Analysis for Financial Time Series
Anna Kiriliouk, Chen Zhou
This book chapter illustrates how to apply extreme value statistics to financial time series data. Such data often exhibits strong serial dependence, which complicates assessment o…
X-Vine Models for Multivariate Extremes
Anna Kiriliouk, Jeongjin Lee, Johan Segers
Regular vine sequences permit the organisation of variables in a random vector along a sequence of trees. Regular vine models have become greatly popular in dependence modelling as…
Multivariate generalized Pareto distributions along extreme directions
Anas Mourahib, Anna Kiriliouk, Johan Segers
When modeling a vector of risk variables, extreme scenarios are often of special interest. The peaks-over-thresholds method hinges on the notion that, asymptotically, the excesses…
Estimating probabilities of multivariate failure sets based on pairwise tail dependence coefficients
Anna Kiriliouk, Chen Zhou
Estimating probabilities of extreme events involving multiple risk factors is a critical challenge in fields such as finance and climate science. This paper proposes a parametric a…
Climate extreme event attribution using multivariate peaks-over-thresholds modeling and counterfactual theory
Anna Kiriliouk, Philippe Naveau
Numerical climate models are complex and combine a large number of physical processes. They are key tools in quantifying the relative contribution of potential anthropogenic causes…
Resampling Procedures with Empirical Beta Copulas
Anna Kiriliouk, Johan Segers, Hideatsu Tsukahara
The empirical beta copula is a simple but effective smoother of the empirical copula. Because it is a genuine copula, from which, moreover, it is particularly easy to sample, it is…
An estimator of the stable tail dependence function based on the empirical beta copula
Anna Kiriliouk, Johan Segers, Laleh Tafakori
The replacement of indicator functions by integrated beta kernels in the definition of the empirical stable tail dependence function is shown to produce a smoothed version of the l…
Hypothesis testing for tail dependence parameters on the boundary of the parameter space
Anna Kiriliouk
Modelling multivariate tail dependence is one of the key challenges in extreme-value theory. Multivariate extremes are usually characterized using parametric models, some of which…
Peaks over thresholds modelling with multivariate generalized Pareto distributions
Anna Kiriliouk, Holger Rootzén, Johan Segers +1
When assessing the impact of extreme events, it is often not just a single component, but the combined behaviour of several components which is important. Statistical modelling usi…
A continuous updating weighted least squares estimator of tail dependence in high dimensions
John H. J. Einmahl, Anna Kiriliouk, Johan Segers
Likelihood-based procedures are a common way to estimate tail dependence parameters. They are not applicable, however, in non-differentiable models such as those arising from recen…
Max-factor individual risk models with application to credit portfolios
Michel Denuit, Anna Kiriliouk, Johan Segers
Individual risk models need to capture possible correlations as failing to do so typically results in an underestimation of extreme quantiles of the aggregate loss. Such dependence…
Nonparametric estimation of extremal dependence
Anna Kiriliouk, Johan Segers, Michal Warchol
There is an increasing interest to understand the dependence structure of a random vector not only in the center of its distribution but also in the tails. Extreme-value theory tac…
An M-estimator of spatial tail dependence
John Einmahl, Anna Kiriliouk, Andrea Krajina +1
Tail dependence models for distributions attracted to a max-stable law are fitted using observations above a high threshold. To cope with spatial, high-dimensional data, a rank-bas…