A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper connects higher order risk measures and stochastic dominance, showing their equivalence and integrating them with optimization.
Constructs new elicitable risk measures with multiplicative scoring functions.
Paper examines risk measure expansions under FGM dependence, improving accuracy at extreme levels.
Investigates a new measure PELVE_n for risk assessment.
A statistical functional, such as the mean or the median, is called elicitable if there is a scoring function or loss function such that the correct forecast of the functional is the unique minimizer of the expected score. Such scoring functions are called strictly consistent for the functional. The elicitability of a …
BScNets expands graph learning to higher-order interactions.
New Gini indices capture more nuanced income inequality.
New method improves credit risk estimation and pricing.
Generative model uses DDPMs for risk-neutral derivative pricing.
Novel higher-order group synchronization for noisy local measurements on hypergraphs.
The paper introduces a new class of multivariate mixtures for actuarial applications.
Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non-Gaussian signal processing and machine learning. In this work, we propose a new similarity me…
A new game-theoretic approach balances downside risk with expected reward.
Hedging methods to mitigate the exposure of variable annuity products to market risks require the calculation of market risk sensitivities (or "Greeks"). The complex, path-dependent nature of these products means these sensitivities typically must be estimated by Monte Carlo simulation. Standard market practice is to m…
We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We develop our copula for first order Markov series, and extend it to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence…
A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucia…
Many nonlinear extensions of the Kalman filter, e.g., the extended and the unscented Kalman filter, reduce the state densities to Gaussian densities. This approximation gives sufficient results in many cases. However, this filters only estimate states that are correlated with the observation. Therefore, sequential esti…
We recover the higher order terms for the acoustic wave equation from measurements of the modulus of the solution. The recovery of these coefficients is reduced to a question of stability for inverting a Hamiltonian flow transform, not the geodesic X-ray transform encountered in other inverse boundary problems like the…
In this paper, a standard PDE for the pricing of arithmetic average strike Asian call option is presented. A Crank-Nicolson Implicit Method and a Higher Order Compact finite difference scheme for this pricing problem is derived. Both these schemes were implemented for various values of risk free rate and volatility. Th…
Study improves BN TTA under distribution shift using higher-order asymptotics.
Method provides formal guarantees for decomposing model uncertainty.
Based on a faithful representation of the heavy tail multivariate distribution of asset returns introduced previously (Sornette et al., 1998, 1999) that we extend to the case of asymmetric return distributions, we generalize the return-risk efficient frontier concept to incorporate the dimensions of large risks embedde…
We show how to measure the failure of the Whitney trick in dimension 4 by constructing higher- order intersection invariants of Whitney towers built from iterated Whitney disks on immersed surfaces in 4-manifolds. For Whitney towers on immersed disks in the 4-ball, we identify some of these new invariants with previous…
Any optimization algorithm based on the risk parity approach requires the formulation of portfolio total risk in terms of marginal contributions. In this paper we use the independence of the underlying factors in the market to derive the centered moments required in the risk decomposition process when the modified vers…
Users form information trails as they browse the web, checkin with a geolocation, rate items, or consume media. A common problem is to predict what a user might do next for the purposes of guidance, recommendation, or prefetching. First-order and higher-order Markov chains have been widely used methods to study such se…
Accounting for the non-normality of asset returns remains challenging in robust portfolio optimization. In this article, we tackle this problem by assessing the risk of the portfolio through the "amount of randomness" conveyed by its returns. We achieve this by using an objective function that relies on the exponential…
Simplicial persistence measures financial market dynamics, revealing long-term structure evolution.
Networks are a fundamental model of complex systems throughout the sciences, and network datasets are typically analyzed through lower-order connectivity patterns described at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, also called network motifs, de…
A model predicts influential nodes in complex networks by considering indirect interactions.
New set-valued star-shaped risk measures introduced for better risk assessment.
Paper characterizes star-shaped risk measures and their properties.
Introduces factor risk measures to assess risk relative to multiple factors.
Unified framework for learning flexible probabilistic programs using DPP and PAC-Bayes bounds.
New method aggregates bootstrapped DAGs for causal discovery.
The paper studies dynamic star-shaped risk measures and their representation.
The paper establishes a connection between different risk measures and their risk contributions.
Improved estimation of higher order integrals using shrinkage techniques.
We apply information-based complexity analysis to support vector machine (SVM) algorithms, with the goal of a comprehensive continuous algorithmic analysis of such algorithms. This involves complexity measures in which some higher order operations (e.g., certain optimizations) are considered primitive for the purposes …
Paper introduces quasi-logconvex risk measures and their properties.
Submodularity is studied for convex risk measures, including Expected Shortfall.
Spectral risk measures are attractive risk measures as they allow the user to obtain risk measures that reflect their risk-aversion functions. To date there has been very little guidance on the choice of risk-aversion functions underlying spectral risk measures. This paper addresses this issue by examining two popular …
The paper explores non-convex risk measures and their characterizations.
Risk measures for multivariate financial positions are studied in a utility-based framework. Under a certain incomplete preference relation, shortfall and divergence risk measures are defined as the optimal values of specific set minimization problems. The dual relationship between these two classes of multivariate ris…
Study risk-sensitive reinforcement learning with Lipschitz dynamic risk measures, establishing regret bounds.
Develops a new method for risk diversification using dynamic risk measures.
Paper introduces new risk measures that unify two existing types.
New risk measures for financial and ESG risks using utility functions.
Dual representations for robust risk measures and uncertainty sets.