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 study tilting subweibull distributions and their tail behavior.
The hidden tail of empirical distributions is analyzed using extreme value theory.
This work extends implicit bias analysis to multiclass classification using a new loss framework.
We introduce a new statistical tool (the TP-statistic and TE-statistic) designed specifically to compare the behavior of the sample tail of distributions with power-law and exponential tails as a function of the lower threshold u. One important property of these statistics is that they converge to zero for power laws o…
New model captures time-varying volatility with stochastic exponential tails.
Study on U-statistics with heavy-tailed samples, providing tail bounds and LDP.
We consider strictly stationary heavy tailed time series whose finite-dimensional exponent measures are concentrated on axes, and hence their extremal properties cannot be tackled using classical multivariate regular variation that is suitable for time series with extremal dependence. We recover relevant information ab…
This work achieves exponential concentration in heavy-tailed data over CAT(κ) spaces using the Fréchet median.
Exponential Lasso improves Lasso's robustness to outliers and heavy-tailed noise.
This work extends diffusion models to handle heavy-tailed targets, improving score estimation and sampling guarantees.
New insights into natural exponential families improve regret bounds for bandit problems.
New algorithms achieve high-probability parameter-free regret in online convex optimization with heavy-tailed data.
The -generalised distribution fits daily stock returns well.
Gradient descent implicitly follows regularization for general losses.
Study on price fluctuations in NFT market, showing heavy-tailed distributions and long-range memory.
A simple log-transform fixes heavy-tailed data for generative models.
New algorithms improve stopping time for best arm identification.
We study dynamical behavior of the Chinese stock markets by investigating the statistical properties of daily ensemble returns and varieties defined respectively as the mean and the standard deviation of the ensemble daily price returns of a portfolio of stocks traded in China's stock markets on a given day. The distri…
Study improves ERM for heavy-tailed data with dependent inputs.
New ensemble method improves model stability exponentially.
SGD converges to an invariant distribution with sub-Gaussian or sub-exponential properties.
We consider a priori generalization bounds developed in terms of cross-validation estimates and the stability of learners. In particular, we first derive an exponential Efron-Stein type tail inequality for the concentration of a general function of n independent random variables. Next, under some reasonable notion of s…
We analyze systems of agents sharing light-tailed risky claims issued by different financial objects. Assuming exponentially distributed claims, we obtain that both agents' and system's losses follow generalized exponential mixture distributions. We show that this leads to qualitatively different results on individual …
Implementing a set of microeconomic criteria, we develop price dynamics equations using a function of demand/supply with key symmetry properties. The function of demand/supply can be linear or nonlinear. The type of function determines the nature of the tail of the distribution based on the randomness in the supply and…
Optimizes regret distribution in stochastic bandits for risk balance.
Paper provides tail bounds for stochastic mirror descent in heavy-tailed noise.
Using a family of modified Weibull distributions, encompassing both sub-exponentials and super-exponentials, to parameterize the marginal distributions of asset returns and their multivariate generalizations with Gaussian copulas, we offer exact formulas for the tails of the distribution of returns of a port…
The paper explores how benign overfitting occurs in heavy-tailed input distributions.
The paper introduces a new method for tail bounds of random vectors and matrices.
Based on the minute-by-minute data of the Hang Seng Index in Hong Kong and the analysis of probability distribution and autocorrelations, we find that the index fluctuations for the first few minutes of daily opening show behaviors very different from those of the other times. In particular, the properties of tail dist…
A random walk on a separable, geodesic hyperbolic metric space converges to the boundary with probability one when the step distribution supports two independent loxodromics. In particular, the random walk makes positive linear progress. Progress is known to be linear with exponential decay when …
We present sharp tail asymptotics for the density and the distribution function of linear combinations of correlated log-normal random variables, that is, exponentials of components of a correlated Gaussian vector. The asymptotic behavior turns out to depend on the correlation between the components, and the explicit s…
We prove exponential decay of correlations for Hölder continuous observables with respect to any Gibbs measure for contact Anosov flows admitting Pesin sets with exponentially small tails. This is achieved by establishing strong spectral estimates for certain Ruelle transfer operators for such flows.
Sharp large deviations and Gibbs conditioning for portfolio credit risk models.
Study shows wealth distribution tails near criticality are not universal.
In this paper a quantitative analysis of the ruin probability in finite time of discrete risk process with proportional reinsurance and investment of finance surplus is focused on. It is assumed that the total loss on a unit interval has a light-tailed distribution -- exponential distribution and a heavy-tailed distrib…
We study random walks on groups with the feature that, roughly speaking, successive positions of the walk tend to be "aligned". We formalize and quantify this property by means of the notion of deviation inequalities. We show that deviation inequalities have several consequences including Central Limit Theorems, the lo…
We provide a detailed study on the implicit bias of gradient descent when optimizing loss functions with strictly monotone tails, such as the logistic loss, over separable datasets. We look at two basic questions: (a) what are the conditions on the tail of the loss function under which gradient descent converges in the…
Modeling financial returns as conditionally independent random variables explains power-law tails.
Bayesian posterior contraction rates improve with decreasing tails
Concentration inequalities form an essential toolkit in the study of high dimensional (HD) statistical methods. Most of the relevant statistics literature in this regard is based on sub-Gaussian or sub-exponential tail assumptions. In this paper, we first bring together various probabilistic inequalities for sums of in…
New stability theory for Sinkhorn semigroups with explicit decay rates.
A robust conformal method for set estimation using non-conformity scores.
Conditional Value-at-Risk (CVaR) is a widely used risk metric in applications such as finance. We derive concentration bounds for CVaR estimates, considering separately the cases of light-tailed and heavy-tailed distributions. In the light-tailed case, we use a classical CVaR estimator based on the empirical distributi…
We introduce and establish the main properties of QHawkes ("Quadratic" Hawkes) models. QHawkes models generalize the Hawkes price models introduced in E. Bacry et al. (2014), by allowing all feedback effects in the jump intensity that are linear and quadratic in past returns. A non-parametric fit on NYSE stock data sho…
Adaptive estimation for nonstationary time series reduces computational cost.
PH-VAE models heavy-tailed data with flexible Phase-Type distributions.
There is accumulating evidence in the literature that stability of learning algorithms is a key characteristic that permits a learning algorithm to generalize. Despite various insightful results in this direction, there seems to be an overlooked dichotomy in the type of stability-based generalization bounds we have in …