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 method detects changes online with bounds on delay.
KQT-EWMA monitors multivariate data streams online with flexible and practical change detection.
Paper proposes a new algorithm to reduce derivative pricing computation time.
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the rela…
A method detects changes in heterogeneous data streams over graph nodes.
A new geometric metric identifies true data changes from parametrization artifacts in high-dimensional representations.
Locally private methods detect changes in time series data.
PERCEPT detects changes in high-dimensional data streams using topological data analysis.
Estimates change points in Weibull time series with copulas.
Framework detects changes in causal dependence between variables.
Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning. Kernel-based nonparametric statistics have been used for this task which enjoy fewer assumptions on the distributions than the parametric approach and can handle high-dimensional data. In this paper we focus on th…
Paper reproduces a kernel-based scan B-statistic for online change-point detection.
This study tackles basis risk in weather parametric insurance using Monte Carlo simulations.
The paper extends Euler class theory to measurable cocycles.
New method detects change points in quasi-periodic signals without supervision.
Improved change point detection using matched filters for non-parametric tests.
Innovative inequalities for divergences with applications in PAC-Bayesian bounds and Monte Carlo.
A new model forecasts financial risks using multiple realized measures.
We analyse derivative securities whose value is NOT a deterministic function of an underlying which means presence of a basis risk at any time. The key object of our analysis is conditional probability distribution at a given underlying value and moment of time. We consider time evolution of this probability distributi…
This paper extends results of Mortimer and Williams (1991) about changes of probability measure up to a random time under the assumptions that all martingales are continuous and that the random time avoids stopping times. We consider locally absolutely continuous measure changes up to a random time, changes of probabil…
Reliable calculations of financial risk require that the fat-tailed nature of prices changes is included in risk measures. To this end, a non-Gaussian approach to financial risk management is presented, modeling the power-law tails of the returns distribution in terms of a Student- (or Tsallis) distribution. Non-Gau…
The multivariate normal density is a monotonic function of the distance to the mean, and its ellipsoidal shape is due to the underlying Euclidean metric. We suggest to replace this metric with a locally adaptive, smoothly changing (Riemannian) metric that favors regions of high local density. The resulting locally adap…
Interpolation improves performance in nearest neighbor algorithms without over-parametrization.
Study parametrized Kähler class for cocycles on Hermitian symmetric spaces.
Develops a nonparametric framework for detecting changes in sequential data.
New algorithm learns nonlinear phenomena from noisy local measurements without data exchange.
New PAC-Bayes bounds derived using Legendre transform and f-divergences.
This paper studies a specific metric on plane curves that has the property of being isometric to classical manifold (sphere, complex projective, Stiefel, Grassmann) modulo change of parametrization, each of these classical manifolds being associated to specific qualifications of the space of curves (closed-open, modulo…
We study the stability of several no-arbitrage conditions with respect to absolutely continuous, but not necessarily equivalent, changes of measure. We first consider models based on continuous semimartingales and show that no-arbitrage conditions weaker than NA and NFLVR are always stable. Then, in the context of gene…
We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we p…
Method identifies shifts leading to large model performance differences.
The study finds that most minimal surfaces in generic 4D manifolds intersect in complex ways.
Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures do not offer an explanation of why neural networks generalize better with over-parametrization. In this work we suggest a novel complexity m…
KCUSUM detects abrupt changes in real-time data streams efficiently.
New insights into binary perceptron reveal phase transitions and algorithmic thresholds.
Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, where there is also demand for probabilistic predictions with measures of uncertainty, the Bayesian additive regression trees (BART) model, i…
Synthetic framework for null hypersurfaces in non-smooth spacetimes.
This paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on …
Recent research has documented a significant rise in the volatility (e.g., expected squared change) of individual incomes in the U.S. since the 1970s. Existing measures of this trend abstract from individual heterogeneity, effectively estimating an increase in average volatility. We decompose this increase in average v…
We find that the CAPM fails to explain the small firm effect even if its non-parametric form is used which allows time-varying risk and non-linearity in the pricing function. Furthermore, the linearity of the CAPM can be rejected, thus the widely used risk and performance measures, the beta and the alpha, are biased an…
LOCA learns standardized data coordinates from measurements.
This paper tackles causal representation learning from multiple distributions without hard interventions.
Understanding and measuring model risk is important to financial practitioners. However, there lacks a non-parametric approach to model risk quantification in a dynamic setting and with path-dependent losses. We propose a complete theory generalizing the relative-entropic approach by Glasserman and Xu to the dynamic ca…
Study adapts AI research methods to analyze image augmentation impacts on neural network operations.
A parametric manifold is a manifold on which all tensor fields depend on an additional parameter, such as time, together with a parametric structure, namely a given (parametric) 1-form field. Such a manifold admits natural generalizations of Lie differentiation, exterior differentiation, and covariant differentiation, …
We study exponential Levy models with change-point which is a random variable, independent from initial Levy processes. On canonical space with initially enlarged filtration we describe all equivalent martingale measures for change-point model and we give the conditions for the existence of f-divergence minimal equival…
The rBergomi model is improved with a regime switching change of measure to match market VIX smiles.
We derive measure change formulae required to price midcurve swaptions in the forward swap annuity measure with stochastic annuities' ratios. We construct the corresponding linear and exponential terminal swap rate pricing models and show how they capture the midcurve swaption correlation skew.