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 of moments estimator goes beyond data reweighting.
Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal, the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods…
New algorithm learns HMM parameters on Riemannian manifolds.
Study uses machine learning to estimate effective policies in settings with hidden individual actions.
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-…
A new method for estimating causal parameters from observables reduces the need for finite moment conditions.
DGMM improves Gaussian mixture modeling efficiency and stability.
Paper develops methods for estimating GLMs and SNR under proportional asymptotics.
We address the problem of estimating the parameters of a time-homogeneous Markov chain given only noisy, aggregate data. This arises when a population of individuals behave independently according to a Markov chain, but individual sample paths cannot be observed due to limitations of the observation process or the need…
We use the expectation of the range of an arithmetic Brownian motion and the method of moments on the daily high, low, opening and closing prices to estimate the volatility of the stock price. The daily price jump at the opening is considered to be the result of the unobserved evolution of an after-hours virtual tradin…
New SGMM algorithm for efficient estimation of moment restriction models.
TGNN combines GNN and SMM for better trading network predictions.
New estimator learns symmetric dynamics from few observations.
A pairs trading model with time-varying volatility using stochastic control.
FedIV uses federated GMM for IV analysis in non-i.i.d. data.
Develops a robust GMM estimator for outlier-tolerant inference.
New framework assesses value of labeled vs unlabeled data in latent variable models.
We present the method of moments approach to pricing barrier-type options when the underlying is modelled by a general class of jump diffusions. By general principles the option prices are linked to certain infinite dimensional linear programming problems. Subsequently approximating those systems by finite dimensional …
Transformer learns to estimate negative binomial parameters efficiently.
Mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations. The current practice for estimating the parameters of such models relies on local search heuristics (e.g., the EM algorithm) which are prone to failure, and existing consistent methods …
New model detects communities in network data from edge nominations.
New algorithm learns permutations mixtures with optimal sample complexity.
Proposes a robust IV estimator using optimal transport for corrupted or adversarial data.
New method for ancestral inference in branching processes with random environments.
This work estimates edge weights of edge-reinforced random walks using observed data.
Efficient policy learning from observational data using weighted classification reductions.
Generative adversarial networks are a novel method for statistical inference that have achieved much empirical success; however, the factors contributing to this success remain ill-understood. In this work, we attempt to analyze generative adversarial learning -- that is, statistical inference as the result of a game b…
A new CI test avoids information loss in discretized data.
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…
Optimal ability estimation in adaptive testing with binary responses.
New method learns near-optimal policies with polynomial samples in A and H.
New estimator improves statistical validity of synthetic data integration.
The Kalman filter and Heston model are used to estimate asset prices and trading performance.
Adaptive t-distribution estimates nonstationary time series using moving moments.
The paper optimizes estimating high-dimensional Gaussian mixtures without separation conditions.
A tensor model for meta-learning adapts to task-specific features.
The objective of this article is to analyze the impact of capital structure on profitability. This impact can be explained by three essential theories: signaling theory, tax theory and the agency costs theory. A sample of 1846 French industrial firms are taken over the period 1999-2006, as a dynamic panel study by usin…
We present an approximated maximum likelihood method for the multifractal random walk processes of [E. Bacry et al., Phys. Rev. E 64, 026103 (2001)]. The likelihood is computed using a Laplace approximation and a truncation in the dependency structure for the latent volatility. The procedure is implemented as a package…
Paper tackles non-identifiability of mixture models in partial order datasets.
Developed moment estimators for affine stochastic volatility models.
Many interesting real world domains involve reinforcement learning (RL) in partially observable environments. Efficient learning in such domains is important, but existing sample complexity bounds for partially observable RL are at least exponential in the episode length. We give, to our knowledge, the first partially …
Current study aims to provide new empirical evidence on the impact of debt on corporate profitability. This impact can be explained by three essential theories: signaling theory, tax theory and the agency cost theory. Using panel data sample of 2240 French non listed companies of service sector during 1999-2006. By uti…
Stochastic Kronecker graphs supply a parsimonious model for large sparse real world graphs. They can specify the distribution of a large random graph using only three or four parameters. Those parameters have however proved difficult to choose in specific applications. This article looks at method of moments estimators…
We develop a behavioral asset pricing model in which agents trade in a market with information friction. Profit-maximizing agents switch between trading strategies in response to dynamic market conditions. Due to noisy private information about the fundamental value, the agents form different evaluations about heteroge…
We present an efficient algorithm for learning mixed membership models when the number of variables is much larger than the number of hidden components . This algorithm reduces the computational complexity of state-of-the-art tensor methods, which require decomposing an tensor, to factorizing…
We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of…
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an "anchor", an observed variable with only that latent variable as its parent. Given such anchors, we show that it is possi…
Agents' heterogeneity is recognized as a driver mechanism for the persistence of financial volatility. We focus on the multiplicity of investment strategies' horizons, we embed this concept in a continuous time stochastic volatility framework and prove that a parsimonious, two-scale version effectively captures the lon…