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.
A new method for estimating causal parameters from observables reduces the need for finite moment conditions.
A new method of moments estimator goes beyond data reweighting.
New method improves estimation of complex models from conditional moment restrictions.
A new method calculates fractional moments using the moment-generating function.
We tackle causal inference under conditional moment restrictions using importance weighting.
We propose a method of moments (MoM) algorithm for training large-scale implicit generative models. Moment estimation in this setting encounters two problems: it is often difficult to define the millions of moments needed to learn the model parameters, and it is hard to determine which properties are useful when specif…
Proposes Moment Exchange to use moments in image recognition models, improving generalization.
New method tightens sub-Gaussian concentration inequalities.
We discuss the probabilistic properties of the variation based third and fourth moments of financial returns as estimators of the actual moments of the return distributions. The moment variations are defined under non-parametric assumptions with quadratic variation method but for the computational tractability, we use …
DGMM improves Gaussian mixture modeling efficiency and stability.
This article investigates parameter estimation of affine term structure models by means of the generalized method of moments. Exact moments of the affine latent process as well as of the yields are obtained by using results derived for p-polynomial processes. Then the generalized method of moments, combined with Quasi-…
New method unfolds distribution moments directly from data without binning.
New SGMM algorithm for efficient estimation of moment restriction models.
The paper uses moment matching method for pricing spread options under Lévy models.
MGD combines maximum entropy and diffusion methods for efficient sampling.
This work develops efficient methods for computing moments of Gaussian mixtures.
Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the individual learning rates. Differently from existing methods, we make use of the most recent first moment of gradients to compute the individual …
Developed moment estimators for affine stochastic volatility models.
Develops a robust GMM estimator for outlier-tolerant inference.
Introduces generalized moment maps for almost Hermitian settings.
Mixture modeling is a general technique for making any simple model more expressive through weighted combination. This generality and simplicity in part explains the success of the Expectation Maximization (EM) algorithm, in which updates are easy to derive for a wide class of mixture models. However, the likelihood of…
We present and analyze a central cutting surface algorithm for general semi-infinite convex optimization problems, and use it to develop a novel algorithm for distributionally robust optimization problems in which the uncertainty set consists of probability distributions with given bounds on their moments. Moments of a…
Paper proposes a new method for density estimation using squared Hellinger distance.
Normal distributions ensure asymptotic variance reduction in moment matching Monte Carlo.
Paper tackles non-convex optimization for higher moments in portfolio management.
We provide an approach for learning deep neural net representations of models described via conditional moment restrictions. Conditional moment restrictions are widely used, as they are the language by which social scientists describe the assumptions they make to enable causal inference. We formulate the problem of est…
GANs learn distributions by matching low-degree moments.
The latest generation of volatility derivatives goes beyond variance and volatility swaps and probes our ability to price realized variance and sojourn times along bridges for the underlying stock price process. In this paper, we give an operator algebraic treatment of this problem based on Dyson expansions and moment …
A new method for generating samples without training, using smoothed score matching.
Corrected moment-based methods improve inference in topic model regression.
This work provides a computationally efficient and statistically consistent moment-based estimator for mixtures of spherical Gaussians. Under the condition that component means are in general position, a simple spectral decomposition technique yields consistent parameter estimates from low-order observable moments, wit…
Framework generates causal probabilities from observational data.
Study uses machine learning to estimate effective policies in settings with hidden individual actions.
Machine learning models accurately predict molecular magnetic anisotropy tensors.
The moments of spatial probabilistic systems are often given by an infinite hierarchy of coupled differential equations. Moment closure methods are used to approximate a subset of low order moments by terminating the hierarchy at some order and replacing higher order terms with functions of lower order ones. For a give…
For a GJR-GARCH specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moment for the most commonly used GARCH models are stated as special cases. We also the limits of these moments as the time hori…
We study generalized moment maps for a Hamiltonian action on a connected compact -twisted generalized complex manifold introduced by Lin and Tolman and prove the convexity and connectedness properties of the generalized moment maps for a Hamiltonian torus action.
In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments. While Adam is an adaptive lower-order moment based (of the stochastic gradient) method, we propose an extension namely, HAdam, which uses higher order moments of the stochastic gradient. Our …
Moment-angle manifolds provide a wide class of examples of non-Kaehler compact complex manifolds. A complex moment-angle manifold Z is constructed via certain combinatorial data, called a complete simplicial fan. In the case of rational fans, the manifold Z is the total space of a holomorphic bundle over a toric variet…
The article introduces inferential moments for analyzing uncertain multivariable systems.
New KCM tests improve specification testing via RKHS.
JME continually estimates data moments privately and accurately.
In this paper, we consider generalized moment maps for Hamiltonian actions on -twisted generalized complex manifolds introduced by Lin and Tolman \cite{Lin}. The main purpose of this paper is to show convexity and connectedness properties for generalized moment maps. We study Hamiltonian torus actions on compact …
The paper classifies various higher moments portfolio optimization methods.
Develops efficient algorithms for learning latent-variable models using implicit moment tensor computation.
We propose a new method of measuring the third and fourth moments of return distribution based on quadratic variation method when the return process is assumed to have zero drift. The realized third and fourth moments variations computed from high frequency return series are good approximations to corresponding actual …
Develops efficient methods for approximating densities of financial models with jumps.
Computation of moments of transformed random variables is a problem appearing in many engineering applications. The current methods for moment transformation are mostly based on the classical quadrature rules which cannot account for the approximation errors. Our aim is to design a method for moment transformation for …