arXiv research

Analyzes premium data of Indian non-life insurers, finding GEV distribution best fits Lognormal and GEV extremes.

New bandit algorithms focus on extreme values, outperforming existing methods.

The thesis evaluates and compares extreme mixture models in finance and insurance.

Study extreme-case Value-at-Risk under IFR distributions, providing guidance for risk management.

Extremal dependence between international stock markets is of particular interest in today's global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, o…

Extreme value theory enhances statistical learning extrapolation for rare events.

Framework reconstructs missing spatio-temporal data for extreme value prediction.

We win EVA2025 by estimating extreme precipitation events using Peaks Over Thresholds and martingale testing.

Survey of extreme value modeling techniques for insurance.

New method uses extreme value theory to estimate neural network errors.

Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a classifier motivated by extreme value theory, addresse…

New framework estimates treatment effects in extreme data.

Study efficient resource allocation for detecting extreme values.

Proposes a network-based strategy to manage financial market risks.

GARCH-UGH improves VaR estimation for financial risk management.

We develop a framework for analyzing extreme values in correlated financial data.

PCA simplifies multivariate extreme data analysis.

Paper improves risk estimation for extreme events.

A novel model combines deep learning and extreme value theory for multivariate cyber risk prediction.

Proposes a method to model financial returns with extreme shocks using flexible tail transformations.

Spectral clustering identifies clusters of multivariate extremes.

Combines GANs and EVT for better modeling of spatial climate extremes.

EX-DRL improves extreme quantile prediction for financial risk management.

Neural network model forecasts extreme flood risk.

The book chapter discusses tail risk analysis for financial data using extreme value statistics.

Extends geometric approach to model non-stationary extremal dependence.

It will be discussed the statistics of the extreme values in time series characterized by finite-term correlations with non-exponential decay. Precisely, it will be considered the results of numerical analyses concerning the return intervals of extreme values of the fluctuations of resistance and defect-fraction displa…

This paper applies the Extreme-Value (EV) Generalised Pareto distribution to the extreme tails of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses tail estimators from these contracts to estimate spectral risk measures, which are coherent risk measures that r…

The paper examines extreme value statistics of high-dimensional sample covariances, with applications in finance and image analysis.

Inference over tails is usually performed by fitting an appropriate limiting distribution over observations that exceed a fixed threshold. However, the choice of such threshold is critical and can affect the inferential results. Extreme value mixture models have been defined to estimate the threshold using the full dat…

Extremal length systole is maximized at the Bolza surface.

The paper tackles catastrophic risk in reinforcement learning using extreme value theory.

ExGAN generates realistic extreme samples using GANs and EVT.

In a wide variety of situations, anomalies in the behaviour of a complex system, whose health is monitored through the observation of a random vector X = (X1,. .. , X d) valued in R d , correspond to the simultaneous occurrence of extreme values for certain subgroups $α$ $\subset$ {1,. .. , d} of variables Xj. Under th…

Kernel PCA helps analyze multivariate extremes and clusters them effectively.

Invariant complex structures on the homogeneous manifold $U(n+1)/U(n)\times U(p+1)/U(p)$ are reseached. Extreme values of sectional curvature of Hermitian metrics on this manifold are found.

The novel unseen classes can be formulated as the extreme values of known classes. This inspired the recent works on open-set recognition \cite{Scheirer_2013_TPAMI,Scheirer_2014_TPAMIb,EVM}, which however can have no way of naming the novel unseen classes. To solve this problem, we propose the Extreme Value Learning (E…

New neural network models extreme value distributions with preserved shape constraints.

We apply the theory of continuous time random walks to study some aspects of the extreme value problem applied to financial time series. We focus our attention on extreme times, specifically the mean exit time and the mean first-passage time. We set the general equations for these extremes and evaluate the mean exit ti…

The paper calculates VaR and CTE for extreme and aggregate risks using FGM copula.

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on o…

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applicat…

Estimates treatment effects in rare extreme events using EVT.

The paper proves extremal black holes form at a critical point of gravitational collapse.

Study tail risk in high-frequency finance using $L_1$-regularized regression.

Efficiently estimates GEV distribution parameters using neural networks.

This paper uses MIS to identify key financial institutions with minimal risk contagion.

Paper introduces SPADE method to protect classifiers from OOD and adversarial samples.