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.
We collect well known and less known facts about the bivariate normal distribution and translate them into copula language. In addition, we prove a very general formula for the bivariate normal copula, we compute Gini's gamma, and we provide improved bounds and approximations on the diagonal.
Random surfaces with boundary have predictable properties.
A new Heckman selection model uses a bivariate contaminated normal distribution for more accurate data analysis.
We show that gamma distributions provide models for departures from randomness since every neighbourhood of an exponential distribution contains a neighbourhood of gamma distributions, using an information theoretic metric topology. We derive also the information geometry of the 3-manifold of McKay bivariate gamma dist…
Study classifies mappings of bivariate normal densities, revealing three types with distinct geometric and statistical properties.
Modeling stock returns and volatility using a bivariate gamma generalized Laplace law.
SkewD robustly discovers causal relationships in skewed noise models.
Develops a new bivariate process for energy markets with improved simulation methods.
Under a generalized skew normal distribution we consider the problem of European option pricing. Existence of the martingale measure is proved. An explicit expression for a given European option price is presented in terms of the cumulative distribution function of the univariate skew normal and the bivariate standard …
PCA is often used in anomaly detection and statistical process control tasks. For bivariate data, we prove that the minor projection (the least varying projection) of the PCA-rotated data is the most sensitive to distributional changes, where sensitivity is defined by the Hellinger distance between distributions before…
Paper simplifies data carving inference with a parametric distribution.
A new CoVaR framework integrates expert views using entropy pooling.
GPDFlow models extreme threshold exceedance with flexible dependence using normalizing flows.
Constructs bivariate quantiles using vine copulas for multivariate analysis.
Method estimates joint distribution of bivariate outcomes.
Gradient-based methods can be biased by distributional asymmetries in bivariate categorical data.
Proposes bivariate DeepKriging for efficient wind field prediction.
Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing m…
New neural networks learn distribution functions using quantiles and moments.
A new class of bivariate distributions is introduced that extends the Generalized Marshall-Olkin distributions of Li and Pellerey (2011). Their dependence structure is studied through the analysis of the copula functions that they induce. These copulas, that include as special cases the Generalized Marshall-Olkin copul…
Worst-case bounds on the expected shortfall risk given only limited information on the distribution of the random variables has been studied extensively in the literature. In this paper, we develop a new worst-case bound on the expected shortfall when the univariate marginals are known exactly and additional expert inf…
The Bivariate Dynamic Contagion Processes (BDCP) are a broad class of bivariate point processes characterized by the intensities as a general class of piecewise deterministic Markov processes. The BDCP describes a rich dynamic structure where the system is under the influence of both external and internal factors model…
The study evaluates financial risk using copulas and statistical tests.
Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and b…
New method distinguishes cause from effect using causal velocity.
New methods optimize sums of bivariate functions on finite domains.
The seemingly disjoint problems of count and mixture modeling are united under the negative binomial (NB) process. A gamma process is employed to model the rate measure of a Poisson process, whose normalization provides a random probability measure for mixture modeling and whose marginalization leads to an NB process f…
We revisit the Kolmogorov-Smirnov and Cramér-von Mises goodness-of-fit (GoF) tests and propose a generalisation to identically distributed, but dependent univariate random variables. We show that the dependence leads to a reduction of the "effective" number of independent observations. The generalised GoF tests are not…
We define parametrized cobordism categories and study their formal properties as bivariant theories. Bivariant transformations to a strongly excisive bivariant theory give rise to characteristic classes of smooth bundles with strong additivity properties. In the case of cobordisms between manifolds with boundary, we pr…
The paper introduces a new model to correct bias in treatment effect estimates due to sample selection.
This paper is dedicated to the consistency of systemic risk measures with respect to stochastic dependence. It compares two alternative notions of Conditional Value-at-Risk (CoVaR) available in the current literature. These notions are both based on the conditional distribution of a random variable Y given a stress eve…
The modelling of empirically observed data is commonly done using mixtures of probability distributions. In order to model angular data, directional probability distributions such as the bivariate von Mises (BVM) is typically used. The critical task involved in mixture modelling is to determine the optimal number of co…
New method improves bivariate causal discovery by accurately estimating cause variable complexity.
MLE and CVE are equivalent under exponential families, leading to faster and more stable EM algorithms.
Levy copulas are the most general concept to capture jump dependence in multivariate Levy processes. They translate the intuition and many features of the copula concept into a time series setting. A challenge faced by both, distributional and Levy copulas, is to find flexible but still applicable models for higher dim…
Study uses a bivariate model to price crude oil futures.
This paper introduces Schur-constant equilibrium distribution models of dimension n for arithmetic non-negative random variables. Such a model is defined through the (several orders) equilibrium distributions of a univariate survival function. First, the bivariate case is considered and analyzed in depth, stressing the…
Enhances U-statistics for semi-supervised datasets using unlabeled data.
Paper introduces a new model for cyber insurance pricing.
This study examines how noise levels affect causal discovery methods.
In this paper we consider a family of Dirac-type operators on fibration equivariant with respect to an action of an etale groupoid. Such a family defines an element in the bivariant theory. We compute the action of the bivariant Chern character of this element on the image of Connes' map in the cyclic…
We study the dependence structure of market states by estimating empirical pairwise copulas of daily stock returns. We consider both original returns, which exhibit time-varying trends and volatilities, as well as locally normalized ones, where the non-stationarity has been removed. The empirical pairwise copula for ea…
The paper develops deep learning models for personalized treatment rules in survival analysis.
Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, i…
DRCD identifies causal direction between continuous and discrete variables using density ratio monotonicity.
Study on merging predictors in causal and anticausal directions using CMAXENT.
New method improves speed of estimating bivariate functional data.
DCK improves air quality index prediction with probabilistic spatial models.