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.
Normal distributions ensure asymptotic variance reduction in moment matching Monte Carlo.
Optimizes MCMC chains with neural control variates.
UCB-V algorithm improves on UCB for MAB problems with variance estimates.
Asymptotic analysis of short-maturity options on realized variance in local-stochastic volatility models.
Estimates Markov chain variance efficiently without storing samples.
In this paper we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic variance of these functionals over suitable classes of control variates. A distinctive feature of the proposed approach is its ability to significantly reduce th…
New strategy optimally identifies best arm in unknown variance Gaussian bandits.
There are many models, often called unnormalized models, whose normalizing constants are not calculated in closed form. Maximum likelihood estimation is not directly applicable to unnormalized models. Score matching, contrastive divergence method, pseudo-likelihood, Monte Carlo maximum likelihood, and noise contrastive…
Large batch sizes reduce gradient variance in DP-SGD, improving privacy.
We develop generic and efficient importance sampling estimators for Monte Carlo evaluation of prices of single- and multi-asset European and path-dependent options in asset price models driven by Lévy processes, extending earlier works which focused on the Black-Scholes and continuous stochastic volatility models. Usin…
This paper is concerned with the asymptotics for Greeks of European-style options and the risk-neutral density function calculated under the constant elasticity of variance model. Formulae obtained help financial engineers to construct a perfect hedge with known behaviour and to price any options on financial assets.
The paper provides concentration inequalities for Markov chain variance estimators.
We study the fair strike of a discrete variance swap for a general time-homogeneous stochastic volatility model. In the special cases of Heston, Hull-White and Schobel-Zhu stochastic volatility models we give simple explicit expressions (improving Broadie and Jain (2008a) in the case of the Heston model). We give condi…
In this note we provide detailed derivations of two versions of small-variance asymptotics for hierarchical Dirichlet process (HDP) mixture models and the HDP hidden Markov model (HDP-HMM, a.k.a. the infinite HMM). We include derivations for the probabilities of certain CRP and CRF partitions, which are of more general…
We provide a simple explicit estimator for discretely observed Barndorff-Nielsen and Shephard models, prove rigorously consistency and asymptotic normality based on the single assumption that all moments of the stationary distribution of the variance process are finite, and give explicit expressions for the asymptotic …
The paper studies stochastic gradient descent with infinite variance gradients.
The paper analyzes how re-weighting helps in reducing variance in high-dimensional kernel methods under covariate shifts.
Linear statistics of random zero sets are integrals of smooth differential forms over the zero set and as such are smooth analogues of the volume of the random zero set inside a fixed domain. We derive an asymptotic expansion for the variance of linear statistics of the zero divisors of random holomorphic sections of p…
This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from [Barndorff-Nielsen et al., 2008] and the quasi-maximum likelihood estimator (QMLE) studied in [Xiu, 2010]. To obtain …
We study confidence intervals based on hard-thresholding, soft-thresholding, and adaptive soft-thresholding in a linear regression model where the number of regressors may depend on and diverge with sample size . In addition to the case of known error variance, we define and study versions of the estimators when…
Improved statistical inference for adaptive Thompson Sampling.
Study variance-reduced method for estimating fixed points in Banach spaces.
Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{é}r-Rao lower bound (efficiency bound), which is the minimum possible variance for an unbiased estimator. However, obtaining such MLE solution requires calculating t…
We study the problem of empirical minimization for variance-type functionals over functional classes. Sharp non-asymptotic bounds for the excess variance are derived under mild conditions. In particular, it is shown that under some restrictions imposed on the functional class fast convergence rates can be achieved incl…
Study on variance of Laplace eigenfunctions on manifolds.
Machine learning reduces variance in online experiment results.
New methods for handling confounding in observational studies.
Markov jump processes (MJPs) are used to model a wide range of phenomena from disease progression to RNA path folding. However, maximum likelihood estimation of parametric models leads to degenerate trajectories and inferential performance is poor in nonparametric models. We take a small-variance asymptotics (SVA) appr…
Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare. The mean-variance function is one of the most widely used objective functions in risk management due to its simplicity and interpretability. Existing algorithms for mean-…
This paper optimizes importance sampling for rare-event options pricing under the Heston model.
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…
Paper proves robust M-estimators' coordinates' normality in high dimensions.
A method for efficient statistical inference from online algorithms.
A new estimator for evaluating policies in unknown environments.
Optimal feature transfer identified through bias-variance analysis.
Paper introduces new risk measures for Kelly criterion.
The paper develops asymptotic theory for QRF variable importance, revealing a bias-variance trade-off.
Proposes HDBEN for heteroscedastic regression with improved sparsity and variance modeling.
Stochastic particle-optimization sampling (SPOS) is a recently-developed scalable Bayesian sampling framework that unifies stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) algorithms based on Wasserstein gradient flows. With a rigorous non-asymptotic convergence theory developed recently…
New methods improve temporal difference learning for policy evaluation in Markov decision processes.
Optimal tuning for estimating ECC in proportional asymptotics.
In the context of the Heston model, we establish a precise link between the set of equivalent martingale measures, the ergodicity of the underlying variance process and the concept of asymptotic arbitrage proposed in Kabanov-Kramkov and in Follmer-Schachermayer.
New Monte Carlo method outperforms existing strategy for estimating Sobol' indices.
Federated learning method improves covariate shift adaptation for missing target values.
This paper analyzes the distribution of genera in 2-bridge knots and proves their asymptotic normality.
We introduce an affine extension of the Heston model where the instantaneous variance process contains a jump part driven by -stable processes with . In this framework, we examine the implied volatility and its asymptotic behaviors for both asset and variance options. Furthermore, we examine the jump clus…
Study on geodesics on random hyperbolic surfaces, showing variance asymptotic to X log X.
Dynamic hedging of an European option under a general local volatility model with small linear transaction costs is studied. A continuous control version of Leland's strategy that asymptotically replicates the payoff is constructed. An associated central limit theorem of hedging error is proved. The asymptotic error va…