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.
Criterion for realizing groups on Enriques manifolds.
Study finds roughness in volatility despite diffusive instantaneous volatility.
New efficient method for inverse Z-transform reduces complexity significantly.
Proves representability of complex semigroup systems.
The paper evaluates forecast accuracy of realized volatility measures in large cross-sections.
Study shows non-spin 4-manifolds where smooth Nielsen realization fails.
We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical…
This article presents an improvement and extension of the heuristic first presented by Hougardy, Lutz, and Zelke in 2010 for realizing triangulated orientable surfaces with few vertices by a simplex-wise linear embedding. The improvement consists in the applicability to non-orientable surfaces (simplex-wise linear imme…
We introduce wavelet-based methodology for estimation of realized variance allowing its measurement in the time-frequency domain. Using smooth wavelets and Maximum Overlap Discrete Wavelet Transform, we allow for the decomposition of the realized variance into several investment horizons and jumps. Basing our estimator…
Boosts weak online learners to strong ones with sublinear regret.
The paper derives formulas for option pricing and random walk expectations.
Deep learning approximates SPDE solutions from noise trajectories.
In the first quarter of 2006 Chicago Board Options Exchange (CBOE) introduced, as one of the listed products, options on its implied volatility index (VIX). This created the challenge of developing a pricing framework that can simultaneously handle European options, forward-starts, options on the realized variance and …
This study connects ReLU neural networks to toric geometry to analyze function realization.
New method models dewetting of anisotropic particles using numerical techniques.
We consider the pricing of derivatives written on the discretely sampled realized variance of an underlying security. In the literature, the realized variance is usually approximated by its continuous-time limit, the quadratic variation of the underlying log-price. Here, we characterize the small-time limits of options…
We present a detailed analysis of \emph{observable} moments based parameter estimators for the Heston SDEs jointly driving the rate of returns and the squared volatilities . Since volatilities are not directly observable, our parameter estimators are constructed from empirical moments of realized volatilitie…
Asymptotic analysis of short-maturity options on realized variance in local-stochastic volatility models.
New integrators preserve geometric structure in Hamiltonian systems.
Flat semigroups can represent normal weighted homogeneous surface singularities.
Given an integer homology class of a finitely presentable group, the systolic volume quantifies how tight could be a geometric realization of this class. In this paper, we study various aspects of this numerical invariant showing that it is a complex and powerful tool to investigate topological properties of homology c…
The affine Grassmannian is a noncompact smooth manifold that parameterizes all affine subspaces of a fixed dimension. It is a natural generalization of Euclidean space, points being zero-dimensional affine subspaces. We will realize the affine Grassmannian as a matrix manifold and extend Riemannian optimization algorit…
New algorithm SELECT minimizes satisficing regret in bandits.
This work establishes a new upper bound on the number of samples sufficient for PAC learning in the realizable case. The bound matches known lower bounds up to numerical constant factors. This solves a long-standing open problem on the sample complexity of PAC learning. The technique and analysis build on a recent brea…
In this paper we will prove that for every integer n>1, there exists a real number H_0<-1 such that every H\in (-\infty,H_0) can be realized as the mean curvature of a embedding of H^{n-1}\times S^1 in the (n+1)-dimensional spaces H^{n+1}. For we explicitly compute the value H_0. For a general value n, we provide…
Hamilton's Ricci flow (RF) equations were recently expressed in terms of a sparsely-coupled system of autonomous first-order nonlinear differential equations for the edge lengths of a d-dimensional piecewise linear (PL) simplicial geometry. More recently, this system of discrete Ricci flow (DRF) equations was further s…
Algorithm identifies bilinear dynamical systems from noisy data.
In this paper, we consider the sparse regularization of manifold-valued data with respect to an interpolatory wavelet/multiscale transform. We propose and study variational models for this task and provide results on their well-posedness. We present algorithms for a numerical realization of these models in the manifold…
A method to fix radius distortion in generative models on curved spaces.
New method estimates VaR and ES using high-frequency data, outperforming existing approaches.
Sinh-acceleration speeds up B-spline option pricing.
Develops a GMM method to estimate roughness in stochastic volatility models.
Paper integrates real data into probabilistic models using Fourier transform.
A new insurance and reinsurance pricing scheme based on realized loss.
In this paper, we consider the variational regularization of manifold-valued data in the inverse problems setting. In particular, we consider TV and TGV regularization for manifold-valued data with indirect measurement operators. We provide results on the well-posedness and present algorithms for a numerical realizatio…
This paper tackles the challenge presented by small-data to the task of Bayesian inference. A novel methodology, based on manifold learning and manifold sampling, is proposed for solving this computational statistics problem under the following assumptions: 1) neither the prior model nor the likelihood function are Gau…
Extracts representative scenarios from large data panels.
Bayesian realized EGARCH models improve tail risk forecasting.
The paper calculates bonus values in complex insurance schemes.
Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measures in detecting direct causal influences or their lack of ability for group selection to capture strong dependencies and accordingly introdu…
The realized GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Further, the realized range, as a competitor for realized variance or daily returns, is employed in the realized GARCH framework. Further, sub-s…
We study realizations of Lie algebras by vector fields. A correspondence between classification of transitive local realizations and classification of subalgebras is generalized to the case of regular local realizations. A reasonable classification problem for general realizations is rigorously formulated and an algori…
A new model framework called Realized Conditional Autoregressive Expectile (Realized-CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a manner analogous to the Realized-GARCH model. Competing realized measures (e.g. Realized Variance and Realized Range) are employed a…
The paper examines circle graphs of Gauss diagrams and finds counterexamples to previous descriptions.
VOLARE provides standardized realized volatility measures from financial data.
Newton's method solves variational problems on manifolds.
Deep neural networks are commonly developed and trained in 32-bit floating point format. Significant gains in performance and energy efficiency could be realized by training and inference in numerical formats optimized for deep learning. Despite advances in limited precision inference in recent years, training of neura…
Incorrect parity-based descriptions of realizable Gauss diagrams found, but bipartite graphs provide a valid approach.