arXiv research

GPDFlow models extreme threshold exceedance with flexible dependence using normalizing flows.

Study compares two methods for predicting extreme atmospheric events.

We study the relaxation dynamics of a financial market just after the occurrence of a crash by investigating the number of times the absolute value of an index return is exceeding a given threshold value. We show that the empirical observation of a power law evolution of the number of events exceeding the selected thre…

New method predicts wave height exceedance probabilities.

Sharp large deviations and Gibbs conditioning for portfolio credit risk models.

The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of this measure is concentrated on a lower dimensional subspace, meaning that certai…

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

We investigate the probability distribution of the return intervals $τ$ between successive 1-min volatilities of two Chinese indices exceeding a certain threshold $q$. The Kolmogorov-Smirnov (KS) tests show that the two indices exhibit multiscaling behavior in the distribution of $τ$, which follows a stretched exponent…

Grover search for optimal portfolios based on Sharpe ratio.

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

The relaxation dynamics of aftershocks after large volatility shocks are investigated based on two high-frequency data sets of the Shanghai Stock Exchange Composite (SSEC) index. Compared with previous relevant work, we have defined main financial shocks based on large volatilities rather than large crashes. We find th…

Investigates multi-period portfolio optimization for DC plans using buffered Probability of Exceedance.

Study large deviations in life insurance portfolios without identical distributions.

Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.

Proposes a model for clearing prices in financial markets due to margin calls.

We investigate the relative information content of six measures of dependence between two random variables $X$ and $Y$ for large or extreme events for several models of interest for financial time series. The six measures of dependence are respectively the linear correlation $ρ^+_v$ and Spearman's rho $ρ_s(v)$ conditio…

We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set. Our algorithm combines reinforcement learning and end-to-end imita…

New study shows diversification can increase risk for heavy-tailed losses.

To meet the Basel II regulatory requirements for the Advanced Measurement Approaches in operational risk, the bank's internal model should make use of the internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. One of the unresolved challeng…

We discuss the statistical properties of index returns in a financial market just after a major market crash. The observed non-stationary behavior of index returns is characterized in terms of the exceedances over a given threshold. This characterization is analogous to the Omori law originally observed in geophysics. …

Being able to forcast extreme volatility is a central issue in financial risk management. We present a large volatility predicting method based on the distribution of recurrence intervals between volatilities exceeding a certain threshold $Q$ for a fixed expected recurrence time $τ_Q$. We find that the recurrence inter…

The statistical properties of the return intervals $τ_q$ between successive 1-min volatilities of 30 liquid Chinese stocks exceeding a certain threshold $q$ are carefully studied. The Kolmogorov-Smirnov (KS) test shows that 12 stocks exhibit scaling behaviors in the distributions of $τ_q$ for different thresholds $q$. …

In this article, we consider a stochastic numerical simulator to assess the impact of some factors on a phenomenon. The simulator is seen as a black box with inputs and outputs. The quality of a simulation, hereafter referred to as fidelity, is assumed to be tunable by means of an additional input of the simulator (e.g…

This study examined how the correlation and network structure of 30 global indices and 145 local Korean indices belonging to the KOSPI 200 have changed during the 13-year period, 2000-2012. The correlations among the indices were calculated. The results showed that although the average correlations of the global indice…

We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from $\el…

Maximal Rate of Stepwise Uncertainty Reduction selects simulations to reduce uncertainty efficiently.

Financial exchanges provide incentives for limit order book (LOB) liquidity provision to certain market participants, termed designated market makers or designated sponsors. While quoting requirements typically enforce the activity of these participants for a certain portion of the day, we argue that liquidity demand t…

Energy markets and the associated energy futures markets play a crucial role in global economies. We investigate the statistical properties of the recurrence intervals of daily volatility time series of four NYMEX energy futures, which are defined as the waiting times $τ$ between consecutive volatilities exceeding a gi…

Study on critical points in random neural networks, revealing three regimes based on activation function.

CAESar improves risk forecasting by combining VaR and ES estimates.

Fragility curves which express the failure probability of a structure, or critical components, as function of a loading intensity measure are nowadays widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii) to evaluate impact of construction details on the structural performance of installations under se…

New algorithm reduces costs and latency for large language model inference.

Improved Hawkes model forecasts extreme financial returns more accurately.

Bayesian method improves extreme quantile estimation with zero coverage error.

This study reduces Willmore flows of tori to simpler problems and finds new conformally constrained Willmore tori.

New algorithm SELECT minimizes satisficing regret in bandits.

We study the statistical properties of the recurrence intervals $τ$ between successive trading volumes exceeding a certain threshold $q$. The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2…

Study on complexity of random polynomials with deterministic spikes, identifying phase transitions.

According to the Loss Distribution Approach, the operational risk of a bank is determined as 99.9% quantile of the respective loss distribution, covering unexpected severe events. The 99.9% quantile can be considered a tail event. As supported by the Pickands-Balkema-de Haan Theorem, tail events exceeding some high thr…

Gradient boosting detects insider purchases predicting abnormal returns in microcap stocks.

Study analyzes cryptocurrency pump-and-dump dynamics using minute-level data.

ES reduces high-probability regret in stochastic linear bandits.

GenFormer uses deep learning to generate complex stochastic data.

xVAE models extreme turbulence events in turbulent flows.

The paper develops sampling methods for ocean phenomena based on temperature and salinity measurements.

Develops deep models to handle nonstationary spatial extremal dependence.

Study optimizes smart contract adoption under high demand variability using Negative Binomial models.

Accurate forecasts of electricity spot prices are essential to the daily operational and planning decisions made by power producers and distributors. Typically, point forecasts of these quantities suffice, particularly in the Nord Pool market where the large quantity of hydro power leads to price stability. However, wh…