arXiv research

Paper evaluates CRPS for extreme event forecasts, finding it unsuitable.

Two BO methods improve reliability optimization for rare failures.

Reply to Tetlock et al. on tail risk and probability gap.

Paper finds robust $Λ$-quantiles equal to extremal distributions.

New model predicts financial tail events using RIA-EVT-Copula.

Being able to predict the occurrence of extreme returns is important in financial risk management. Using the distribution of recurrence intervals---the waiting time between consecutive extremes---we show that these extreme returns are predictable on the short term. Examining a range of different types of returns and th…

New framework assesses extreme errors in machine learning models.

ExGAN generates realistic extreme samples using GANs and EVT.

Estimates extreme probabilities using fewer simulations than Monte Carlo.

SS-GEN simulates rare events in heavy and light-tailed data.

Study compares two methods for predicting extreme atmospheric events.

Paper develops a neural model to assess cascading extreme events.

Develops RES metrics for stable rare-event forecasting evaluation.

We show a general relation between the spatially disjoint product of probability density functions and the sum of their Fisher information metric tensors. We then utilise this result to give a method for constructing the probability density functions for an arbitrary Riemannian Fisher information metric tensor. We note…

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

We consider a controlled diffusion process $(X_t)_{t\ge 0}$ where the controller is allowed to choose the drift $μ_t$ and the volatility $σ_t$ from a set $\K(x) \subset \R\times (0,\infty)$ when $X_t=x$. By choosing the largest $\fracμ{σ^2}$ at every point in time an extremal process is constructed which is under suita…

We study cross-country GDP losses due to financial crises in terms of frequency (number of loss events per period) and severity (loss per occurrence). We perform the Loss Distribution Approach (LDA) to estimate a multi-country aggregate GDP loss probability density function and the percentiles associated to extreme eve…

This paper explores the possibility that asset prices, especially those traded in large volume on public exchanges, might comply with specific physical laws of motion and probability. The paper first examines the basic dynamics of asset price displacement and finds one can model this dynamic as a harmonic oscillator at…

The hidden tail of empirical distributions is analyzed using extreme value theory.

Improved bounds for discrete probability distribution estimation under the ℓ∞ norm.

New methods speed up fitting for large datasets with noisy observations.

Distillation improves simple models by approximating complex labels.

The purpose of this paper is to analyze the isoperimetric inequality for symmetric log-convex probability measures on the line. Using geometric arguments we first re-prove that extremal sets in the isoperimetric inequality are intervals or complement of intervals (a result due to Bobkov and Houdré). Then we give a quan…

We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence. We do not ass…

Deep learning framework predicts streamflow and flood probabilities in Australian catchments.

Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e.g. portfolio monitoring, insurance, environmental risk management and anomaly detection. One convenient (non-parametric) characterization of extremal de…

This article provides a new toolbox to derive sparse recovery guarantees from small deviations on extreme singular values or extreme eigenvalues obtained in Random Matrix Theory. This work is based on Restricted Isometry Constants (RICs) which are a pivotal notion in Compressed Sensing and High-Dimensional Statistics a…

Neural network model forecasts extreme flood risk.

Study optimizes sampling to avoid extreme tail risks in unknown heavy-tailed distributions.

A new notion of stochastic ordering is introduced to compare multivariate stochastic risk models with respect to extreme portfolio losses. In the framework of multivariate regular variation comparison criteria are derived in terms of ordering conditions on the spectral measures, which allows for analytical or numerical…

Proposes a new framework to manage venture capital portfolio risk by focusing on deal-level correlations.

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

New method clusters and visualizes anomalies in complex systems.

The probability distribution function (PDF) for prices on financial markets is derived by extremization of Fisher information. It is shown how on that basis the quantum-like description for financial markets arises and different financial market models are mapped by quantum mechanical ones.

This paper develops a theory of Lipschitz comparisons of hyperbolic surfaces analogous to the theory of quasi-conformal comparisons. Extremal Lipschitz maps (minimal stretch maps) and geodesics for the `Lipschitz metric' are constructed. The extremal Lipschitz constant equals the maximum ratio of lengths of measured la…

New methods for estimating causal effects with limited overlap, using Stable Probability Weighting.

The paper analyzes extreme risk measures with limited distributional information.

New model estimates corporate defaults using pure jump processes, capturing extreme events.

We propose a family of models that enable predictive estimation of time-varying extreme event probabilities in heavy-tailed and nonlinearly dependent time series. The models are a white noise process with conditionally log-Laplace stochastic volatility. In contrast to other, similar stochastic volatility formalisms, th…

Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study ma…

Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, ou…

Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evide…

We construct an infinite-dimensional information manifold based on exponential Orlicz spaces without using the notion of exponential convergence. We then show that convex mixtures of probability densities lie on the same connected component of this manifold, and characterize the class of densities for which this mixtur…

The paper shows real market exists free lunches with vanishing risks.

The paper integrates behavioral distortions into portfolio optimization using implied probability weighting functions.

We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs). By the application of automatic differentiation with respect to response variables and then to parameters of this CDF representation, we are able to build bl…

Study shows one-dimensional location-scale-shape models are flat in Wasserstein geometry.

Forest tree species mapped with high accuracy using satellite data.