arXiv research

This paper calculates risk-dependent centrality of Brazilian stocks, showing rankings vary with external risk and crisis events.

Node centrality is one of the most important and widely used concepts in the study of complex networks. Here, we extend the paradigm of node centrality in financial and economic networks to consider the changes of node "importance" produced not only by the variation of the topology of the system but also as a consequen…

This paper studies the problem of nonparametric estimation of a smooth function with data distributed across multiple machines. We assume an independent sample from a white noise model is collected at each machine, and an estimator of the underlying true function needs to be constructed at a central machine. We place l…

We study an optimal investment/consumption problem in a model capturing market and credit risk dependencies. Stochastic factors drive both the default intensity and the volatility of the stocks in the portfolio. We use the martingale approach and analyze the recursive system of nonlinear Hamilton-Jacobi-Bellman equatio…

New model captures insurance risk dependencies efficiently.

Optimal reinsurance contracts for multiple dependent risks are derived without specific dependency assumptions.

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

New approach improves classification guarantees by focusing on direction rather than regression risk.

It is commonly accepted that Commodities futures and forward prices, in principle, agree under some simplifying assumptions. One of the most relevant assumptions is the absence of counterparty risk. Indeed, due to margining, futures have practically no counterparty risk. Forwards, instead, may bear the full risk of def…

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, the insurers have t…

New method estimates insurance risk dependencies.

In this paper, we address the aggregation of dependent stop loss reinsurance risks where the dependence among the ceding insurer(s) risks is governed by the Sarmanov distribution and each individual risk belongs to the class of Erlang mixtures. We investigate the effects of the ceding insurer(s) risk dependencies on th…

Paper analyzes self-supervised image denoising with denatured data.

Compact formulas for evaluating insurance policies' risks.

Paper proposes optimal investment and reinsurance strategies considering financial and insurance risks dependence.

Paper introduces DCoVaR for aggregate risk models, outperforming existing methods.

New bounds for multi-task learning with varying task sizes.

High-dimensional shrinkage risk depends on the default prior for the common scale.

Neural network model forecasts extreme flood risk.

The problem of adaptive noisy clustering is investigated. Given a set of noisy observations $Z_i=X_i+ε_i$, $i=1,...,n$, the goal is to design clusters associated with the law of $X_i$'s, with unknown density $f$ with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach as the popular …

The paper tests if optimal hedge ratios for Bitcoin are position-dependent.

The paper introduces fixed-point centralities for networks and graphons.

Study optimizes estimating linear functionals from observational data without strict overlap.

Study tail risk aggregation under dependence uncertainty.

Developing an Agent-Based Model to Mitigate Adverse Selection in Uniswap v3 Liquidity Providers

As relational datasets modeled as graphs keep increasing in size and their data-acquisition is permeated by uncertainty, graph-based analysis techniques can become computationally and conceptually challenging. In particular, node centrality measures rely on the assumption that the graph is perfectly known -- a premise …

The problem of estimating a high-dimensional sparse vector $\boldsymbolθ \in \mathbb{R}^n$ from an observation in i.i.d. Gaussian noise is considered. The performance is measured using squared-error loss. An empirical Bayes shrinkage estimator, derived using a Bernoulli-Gaussian prior, is analyzed and compared with the…

A hypersurface $M$ in $\mathbb{R}^n$, $n \geq 4$, has central ovaloid property if $M$ intersects some hyperplane transversally along an ovaloid and every such ovaloid on $M$ has central symmetry. We show that a complete, connected, smooth hypersurface with central ovaloid property must either be a cylinder over a centr…

Central banks play a key role in promoting sustainable finance.

The purpose of this paper is to show how central extensions of (possibly infinite-dimensional) Lie algebras integrate to central extensions of étale Lie 2-groups. In finite dimensions, central extensions of Lie algebras integrate to central extensions of Lie groups, a fact which is due to the vanishing of π_2 for each …

Constructs examples of centrally harmonic spaces and shows they are not generically harmonic.

This paper identifies and bounds ICE central moments using PO marginal central moments.

Study shows volume density in central harmonic spaces can vary arbitrarily.

We determine the universal central extension of the Lie algebra of hamiltonian vector fields, thereby classifying its central extensions. Furthermore, we classify the central extensions of the Lie algebra of symplectic vector fields, of the Poisson Lie algebra, and of its compactly supported version.

Study Drinfeld centralizers and Rouquier complexes in homotopy categories.

Centralized exchanges influence staking behavior and decentralization in Proof of Stake blockchain ecosystems.

Criterion for lifting smooth contact maps between Carnot groups to central extensions.

Paper breaks down risk contribution into inherent and correlation risk components.

FUSE neural centrality framework improves data point measurement in high dimensions.

We prove: If a complete connected smooth surface M in euclidean 3-space has general position, intersects some plane along a clean figure-8 (a loop with total curvature zero) and all compact intersections with planes have central symmetry, then M is a (geometric) cylinder over some central figure-8. On the way, we estab…

Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not…

We show existence of centrally symmetric maps on surfaces all of whose faces are quadrangles and pentagons for each orientable genus $g \geq 0$. We also show existence of centrally symmetric maps on surfaces all of whose faces are hexagons for each orientable genus $g = 2k-1$, $k\in \mathbb{N}$. We enumerate centrally …

Paper proves CLTs for Q-learning with asynchronous updates.

We construct a sequence of commuting central affine curve flows on $R^n\backslash 0$ invariant under the action of $SL(n,R)$ and prove the following results: (a) The central affine curvatures of a solution of the j-th central affine curve flow is a solution of the j-th flow of Gelfand-Dickey (GD$_n$) hierarchy on the s…

The paper validates a centrality measure for financial networks during financial distress.

MakerDAO's governance is centralized despite its decentralized claim.

Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are built in a centralized fashion. One bottleneck of centralized algorithms lies on high communication cost on the central node. Motivated by this, we ask, can decentralized algorithms be faster than its centralized counterpart? Althoug…

Each compact Riemannian manifold with no conjugate points admits a family of functions whose integrals vanish exactly when central Busemann functions split linearly. These functions vanish when all central Busemann functions are sub- or superharmonic. When central Busemann functions are convex or concave, they must be …