arXiv research

The study improves volatility model pricing accuracy with new statistical expansions.

The paper provides non-asymptotic Edgeworth expansions for neural network outputs.

We analyze the semi-hard triplet loss using Edgeworth expansion for better understanding of its behavior.

The validity of an approximation formula for European option prices under a general stochastic volatility model is proved in the light of the Edgeworth expansion for ergodic diffusions. The asymptotic expansion is around the Black-Scholes price and is uniform in bounded payoff func- tions. The result provides a validat…

Sharp privacy bounds for sequential analysis of sensitive data.

In this paper, we study the Edgeworth expansion for a pre-averaging estimator of quadratic variation in the framework of continuous diffusion models observed with noise. More specifically, we obtain a second order expansion for the joint density of the estimators of quadratic variation and its asymptotic variance. Our …

Paper provides Edgeworth expansions for network moments, improving accuracy of sampling distributions.

Edgeworth Accountant calculates privacy loss under differential privacy compositions efficiently.

Bayesian inference for wide neural networks using Edgeworth expansion.

New method improves nonlinear filtering accuracy with reduced computation.

Study finds the number of modes in Gaussian kernel density estimators scales with sqrt(β log β).

Study improves BN TTA under distribution shift using higher-order asymptotics.

Cumulant expansion is used to derive accurate closed-form approximation for Monthly Sum Options in case of constant volatility model. Payoff of Monthly Sum Option is based on sum of $N$ caped (and probably floored) returns. It is noticed, that $1/\sqrt{N}$ can be used as a small parameter in Edgeworth expansion. First …

A small-time Edgeworth expansion of the density of an asset price is given under a general stochastic volatility model, from which asymptotic expansions of put option prices and at-the-money implied volatilities follow. A limit theorem for at-the-money implied volatility skew and curvature is also given as a corollary.…

This paper demonstrates the efficiency of using Edgeworth and Gram-Charlier expansions in the calibration of the Libor Market Model with Stochastic Volatility and Displaced Diffusion (DD-SV-LMM). Our approach brings together two research areas; first, the results regarding the SV-LMM since the work of Wu and Zhang (200…

The paper provides rigorous guarantees for m-out-of-n bootstrap estimators of sample quantiles.

We present a flexible approach for the valuation of interest rate derivatives based on Affine Processes. We extend the methodology proposed in Keller-Ressel et al. (2009) by changing the choice of the state space. We provide semi-closed-form solutions for the pricing of caps and floors. We then show that it is possible…

We derive a new, exact and transparent expansion for option smiles, which lends itself both to analytical approximation and, perhaps more importantly, to congenial numerical treatments. We show that the skew and the curvature of the smile can be computed as exotic options, for which the Hedged Monte Carlo method is par…

A new model for pricing ultra-short-term options with complex volatility patterns.

There has been a recent surge of interest in modeling neural networks (NNs) as Gaussian processes. In the limit of a NN of infinite width the NN becomes equivalent to a Gaussian process. Here we demonstrate that for an ensemble of large, finite, fully connected networks with a single hidden layer the distribution of ou…

New bootstraps improve speed and accuracy for graph count functionals.

Paper develops a method to compare generative models using KL divergence.

The paper calculates heat kernel and closed geodesic asymptotics for nilpotent coverings.

Paper estimates spectral risk measures for insurance data with truncated and censored data.

The paper improves A/B testing for non-Gaussian data, ensuring reliable results with large sample sizes.

Rescaling expansiveness proven for k*-expansive vector fields.

The paper derives expansions for Green's operators and resolvents using Hadamard methods.

Develops a martingale expansion for stochastic volatility models.

Taylor expansions improve reinforcement learning policies.

Analytic torsion expansions for symmetric and complex homogeneous spaces.

Proved cyclotomic expansion for double twist knots' HOMFLY-PT invariants.

Study of hypersurfaces with specific expansion properties.

A new hypergraph expansion method treats vertices and hyperedges equally, improving node classification.

The paper calculates asymptotic expansions for specific types of oscillatory integrals.

Dropout increases the generalization of neural networks by expanding the weight space.

New derivation of knot invariants from universal invariant.

This work explores functional expansions to handle path dependence in various fields.

In the planar limit of the 't Hooft expansion, the Wilson-loop average in 3d Chern-Simons theory (i.e. the HOMFLY polynomial) depends in a very simple way on representation (the Young diagram), so that the (knot-dependent) Ooguri-Vafa partition function becomes a trivial KP tau-function. We study higher genus correctio…

Paper calculates third coefficient in Kaehler-Einstein metric expansion.

In this paper we introduce kinetic equations for the evolution of the probability distribution of two goods among a huge population of agents. The leading idea is to describe the trading of these goods by means of some fundamental rules in price theory, in particular by using Cobb-Douglas utility functions for the bina…

We describe the first known mean-field study of landing probabilities for random walks on hypergraphs. In particular, we examine clique-expansion and tensor methods and evaluate their mean-field characteristics over a class of random hypergraph models for the purpose of seed-set community expansion. We describe paramet…

Study on heat trace expansion for thermoelastic Dirichlet-to-Neumann map.

The paper proposes and proves asymptotic expansions for quantum invariants.

New method models portfolios with leptokurtic risk factors using Gram-Charlier expansions.

Develops AMITE for analyzing neural network nonlinearities.

We survey recent results about the asymptotic expansion of Toeplitz operators and their kernels, as well as Berezin-Toeplitz quantization. We deal in particular with calculation of the first coefficients of these expansions.

Paper presents new expansions for option pricing with cash dividends.

The paper uses polyhedral expansions to capture the shape of compact metric spaces.