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.
New estimator reveals intraday betas mainly driven by correlations.
We define scenarios, propose different methods of aggregating them, discuss their properties and benchmark them against quadrant requirements.
Proposes a method to measure similarity between anomaly scores from different methods.
Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we …
We introduce a new framework for training deep generative models for high-dimensional conditional density estimation. The Bottleneck Conditional Density Estimator (BCDE) is a variant of the conditional variational autoencoder (CVAE) that employs layer(s) of stochastic variables as the bottleneck between the input a…
Consider two insurance companies (or two branches of the same company) that receive premiums at different rates and then split the amount they pay in fixed proportions for each claim (for simplicity we assume that they are equal). We model the occurrence of claims according to a Poisson process. The ruin is achieved wh…
Copula models have become popular in different applications, including modeling shocks, in view of their ability to describe better the dependence concepts in stochastic systems. The class of maxmin copulas was recently introduced by Omladič and Ružić. It extends the well known classes of Marshall-Olkin and Marshall co…
Differential forms and symmetric tensors show contrasting singular behaviors in a specific geometric setting.
In this article we consider the Merton problem in a market with a single risky asset and transaction costs. We give a complete solution of the problem up to the solution of a free-boundary problem for a first-order differential equation, and find that the form of the solution (whether the problem is well-posed, whether…
We factorize the Dirac operator on the Connes-Landi 4-sphere in unbounded KK-theory. We show that a family of Dirac operators along the orbits of the torus action defines an unbounded Kasparov module, while the Dirac operator on the principal orbit space -an open quadrant in the 2-sphere- defines a half-closed chain. W…
We exhibit many examples of closed symplectic manifolds on which there is an autonomous Hamiltonian whose associated flow has no nonconstant periodic orbits (the only previous explicit example in the literature was the torus T^2n (n\geq 2) with an irrational symplectic structure). The underlying smooth manifolds of our…
The quotient of random variables with normal distributions is examined and proven to have have power law decay, with density , with the coefficient depending on the means and variances of the numerator and denominator and their correlation. We also obtain the conditional probability…
In this paper we introduce some new copulas emerging from shock models. It was shown earlier that reflected maxmin copulas (RMM for short) are not just some specific singular copulas; they contain many important absolutely continuous copulas including the negative quadrant dependent part of the Eyraud-Farlie-Gumbel-Mor…
New insights into tail behavior of heavy-tailed random vectors and processes.
A new optimization method for probability simplex problems.
GCNs help in diagnosing label scarcity and feature quality on graphs.
The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents. Over the years, various methods of analysing rodent data collected in this task have been proposed. These methods span from classical performance measurements (e.g. escape latency, rodent speed, quadrant p…
Study examines financial market structure changes during the COVID-19 crash using a novel MI approach.
In this paper, we propose the use of a black-box optimization method called deterministic Mesh Adaptive Direct Search (MADS) algorithm with orthogonal directions (Ortho-MADS) for the selection of hyperparameters of Support Vector Machines with a Gaussian kernel. Different from most of the methods in the literature that…
Let M be a complete n-dimensional Riemannian spin manifold, partitioned by q two-sided hypersurfaces which have a compact transverse intersection N and which in addition satisfy a certain coarse transversality condition. Let E be a Hermitean bundle with connection on M. We define a coarse multi-partitioned index of the…
FLAIR measures LP competitiveness in AMMs, improving LP performance evaluations.
New estimators outperform maximum likelihood without hyper-parameter estimation.
New estimator reduces kernel mean estimation error.
Dual Bayesian Affine Estimators for Wiener-type state-space models
Enhances gradient estimates for Hermitian Monge-Ampère equations.
Paper proposes robust estimators for GANs under Wasserstein contamination.
New framework converts offline to online estimation using black-box offline estimators.
Proposes variational autoencoder for efficient MMSE estimation.
Paper improves Fisher information estimation methods.
Proposes a robust estimator for RD designs.
New estimator reduces variance in discrete random variables.
SCOPE estimator improves covariance and precision matrix estimation.
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm results in a convex combination of the single-task maximum likelihood estimates. We derive the optimal minimum risk estimator and the minimax estimator, and show t…
Obtaining more accurate equity value estimates is the starting point for stock selection, value-based indexing in a noisy market, and beating benchmark indices through tactical style rotation. Unfortunately, discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estim…
The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties. Being able to efficiently estimate the variance of this estimator is very helpful to vario…
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on discretely sampled continuo…
A new copula estimation method using classification.
This paper reviews SDR methods for multivariate response regression.
Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation procedure can be vulnerable to corrupted data points, which often pushes the estimated ratio toward infinity. In this paper, we present a robust estimator wh…
TAKDE optimizes kernel density estimation for real-time dynamic processes.
We introduce two new estimators of the bivariate Hurst exponent in the power-law cross-correlations setting -- the cross-periodogram and local -Whittle estimators -- as generalizations of their univariate counterparts. As the spectrum-based estimators are dependent on a part of the spectrum taken into consideration …
Paper bridges score estimation to parameter and density estimation in DDPMs.
New method for fast volatility estimation robust to change points.
ROME improves density estimation for multi-modal, non-normal data.
Paper introduces VDE, a variance-reduced determinant estimator.
New estimator improves mutual information estimation.
Private estimation of many quantiles using differential privacy.
Paper proposes robust LAD estimators for 2D sinusoidal model, proving consistency and normality.