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arXiv research

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.

168,742 papers · 148 categories

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25.0%50.0%75.0%100.0% · May 199319922001200920172026
48 results for first order expansion

We develop a first order expansion for convex penalized estimators in high-dimensional regression.

problem High-dimensional regression problems with random designs.
method Construct a first order expansion ηη of the penalized estimator β^\hatβ.
result The risk of β^\hatβ is asymptotically the same as the risk of ηη.

The paper develops an expansion for optimizing portfolios with small quadratic transaction costs.

problem Optimizing portfolios with small, instantaneous, quadratic transaction costs.
method Develops an asymptotic expansion for the Hamilton-Jacobi-Bellman equation.
result Derives explicit formulae for the first two terms of the expansion.

Paper examines risk measure expansions under FGM dependence, improving accuracy at extreme levels.

problem Capturing higher-order tail behavior and dependence effects in risk measures.
method Second-order asymptotic expansions using extreme value theory and regular variation theory.
result Second-order approximations reduce approximation errors, especially at extreme confidence levels.

We derive a small-time expansion for out-of-the-money call options under an exponential Levy model, using the small-time expansion for the distribution function given in Figueroa-Lopez & Houdre (2009), combined with a change of numéraire via the Esscher transform. In particular, we quantify find that the effect of a no…

2011-05-16abs ↗pdf ↗

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

problem Accurate descriptions of sampling distributions of network moment statistics.
method Edgeworth expansion applied to studentized network moment statistics.
result Higher-order accurate approximation to sampling CDF of network moment statistics.

We obtain a first order extension of the large deviation estimates in the Gärtner-Ellis theorem. In addition, for a given family of measures, we find a special family of functions having a similar Laplace principle expansion up to order one to that of the original family of measures. The construction of the special fam…

2014-06-14abs ↗pdf ↗

In the framework of an incomplete financial market where the stock price dynamics are modeled by a continuous semimartingale (not necessarily Markovian) an explicit second-order expansion formula for the power investor's value function - seen as a function of the underlying market price of risk process - is provided. T…

2014-10-03abs ↗pdf ↗

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

problem Understanding the behavior of the semi-hard triplet loss function.
method Developed a higher-order asymptotic analysis using the Edgeworth expansion.
result Derived explicit Edgeworth expansions revealing first-order corrections in terms of the third cumulant.

We consider a financial market with liquidity cost as in Çetin, Jarrow and Protter [2004], where the supply function Sε(s,ν)S^ε(s,ν) depends on a parameter ε0ε\geq 0 with S0(s,ν)=sS^0(s,ν)=s corresponding to the perfect liquid situation. Using the PDE characterization of Çetin, Soner and Touzi [2010] of the super-hedging cost of a…

2012-08-18abs ↗pdf ↗

We consider second-order elliptic partial differential operators acting on sections of vector bundles over a compact Riemannian manifold without boundary, working without the assumption of Laplace-like principal part NμNμ-\N^μ\N_μ. Our objective is to obtain information on the asymptotic expansions of the corresponding r…

1999-05-03abs ↗pdf ↗

Develops a new framework to analyze gradient flow regimes and derive explicit solutions.

problem Analyzing scaling regimes and deriving explicit analytic solutions for gradient flow in large learning problems.
method Formal power series expansion of the loss evolution with coefficients encoded by diagrams.
result Reveals different learning phases and obtains explicit solutions in some cases.

Roy's `Safety First' criterion for selecting one risky asset from many is adapted to the case of non-normal returns, via Cornish Fisher expansion. The resulting investment objective is consistent with first order stochastic dominance, and is equal to the Sharpe ratio for the case of normal returns. An investor selectin…

2015-06-13abs ↗pdf ↗

Unified framework for adaptive learning systems using consolidation and expansion operations.

problem Managing the balance between consolidating known knowledge and expanding into new evidence in adaptive learning systems.
method Introduces Consolidation-Expansion Operator Mechanics (OpMech) with the order-gap metric to control the balance.
result The order-gap signal provides real-time control and termination guarantees for adaptive learning systems.

Connectedness of small clusters in Riemannian and Finsler manifolds proven.

problem Understanding connectedness of small clusters in Riemannian and Finsler manifolds.
method Proved connectedness and small diameter properties for clusters of small volume in both manifolds.
result Clusters in Riemannian manifolds are connected and have small diameter; in Finsler manifolds, they are at most m connected components of small diameter.

Improved stochastic approximation method reduces residual error.

problem Reducing residual error in stochastic approximation algorithms.
method Fixed-schedule one-quarter barrier and bias-corrected acceleration.
result Achieves T1/2+o(1)T^{-1/2+o(1)} residual reduction with O(1)O(1) primitive samples.

We review the utility-based valuation method for pricing derivative securities in incomplete markets. In particular, we review the practical approach to the utility-based pricing by the means of computing the first order expansion of marginal utility-based prices with respect to a small number of random endowments.

2010-03-30abs ↗pdf ↗

The paper studies the asymptotic expansion of Gaussian integral operators on Riemannian submanifolds.

problem Analyzing the asymptotic behavior of Gaussian integral operators on Riemannian submanifolds.
method Deriving a full asymptotic expansion of the Gaussian integral operator and computing the first-order correction term.
result Explicit computation of the first-order correction term in terms of mean curvature vector and scalar curvature.

It is known that Heston's stochastic volatility model exhibits moment explosion, and that the critical moment s+s_+ can be obtained by solving (numerically) a simple equation. This yields a leading order expansion for the implied volatility at large strikes: σBS(k,T)2TΨ(s+1)×kσ_{BS}( k,T)^{2}T\sim Ψ(s_+-1) \times k (Roger Lee's moment…

2010-01-18abs ↗pdf ↗

Iterative tilting fine-tunes diffusion models for reward-tilted distributions.

problem Fine-tuning diffusion models for reward-tilted distributions.
method Decomposes large reward tilts into smaller, tractable tilts via first-order Taylor expansion, avoiding backpropagation.
result Validated on a two-dimensional Gaussian mixture, achieving exact closed-form solutions.

This paper is devoted to a third order study of the end-point map in sub-Riemannian geometry. We first prove third order open mapping results for maps from a Banach space into a finite dimensional manifold. In a second step, we compute the third order term in the Taylor expansion of the end-point map and we specialize …

2019-07-25abs ↗pdf ↗

Investor optimizes wealth in a market with non-traded endowment, deriving expansions up to second order.

problem Optimizing wealth in an incomplete financial market with a non-traded endowment.
method Duality techniques and Kunita-Watanabe projections for deriving expansions up to second order.
result Derives expansions of the primal value function and optimal wealth process up to second order with respect to the non-traded endowment units.

Paper tackles high-order inference in structured prediction tasks.

problem Maximizing a score function on the space of labels in high-order Markov random fields.
method Generative model approach with two-stage convex optimization algorithm.
result Success in general high-order inference problems driven by hyperedge expansion properties.

The paper optimizes portfolios in a financial market with correlated assets using a stochastic volatility model.

problem Optimizing portfolios in a financial market with correlated assets and stochastic volatility.
method Derive a Hamilton-Jacobi-Bellman equation, use approximation methods, analyze value function using expansion of utility function, control error with second-order terms, generate close-to-optimal portfolio.
result Close-to-optimal portfolio generated using first-order approximation of utility function with controlled error.

Paper develops formulas for shape derivatives in wave scattering.

problem Computing high order shape derivatives for wave scattering is challenging.
method Introduces elegant recurrence formulas using differential forms and Lie derivatives.
result Unified framework for computing high order shape perturbations in scattering problems.

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

problem Approximating deviations of finite-width neural networks from their Gaussian limit.
method Multidimensional Edgeworth expansions of arbitrary order for neural network outputs.
result Established a bound on the total variation distance between neural network output and its Edgeworth approximation.

Study local expansions of continuous-time processes using Ito signature properties.

problem Analyzing local expansions of continuous-time processes and their moments.
method Using the Ito signature, a basis of iterated integrals, to conduct expansions of the process' characteristic function.
result Explicit coefficients and stochastic representations for asymptotics as time shrinks or diverges.

Study asymptotic properties of generalized shortfall risk measures for heavy-tailed risks.

problem Understanding risk measures for heavy-tailed risks.
method Derive asymptotic expansions for generalized shortfall risk measures.
result Unified theory for risk measures including distortion and utility-based measures.

In this paper we prove an approximate formula expressed in terms of elementary functions for the implied volatility in the Heston model. The formula consists of the constant and first order terms in the large maturity expansion of the implied volatility function. The proof is based on saddlepoint methods and classical …

2009-11-16abs ↗pdf ↗

In this work we consider the Taylor expansion of the exponential map of a submanifold immersed in R^n up to order three, in order to introduce the concepts of lateral and frontal deviation. We compute the directions of extreme lateral and frontal deviation for surfaces in R^3. Also we compute, by using the Taylor expan…

2012-10-22abs ↗pdf ↗

Sharp Sobolev inequality derived for Riemannian manifolds with bounded Ricci curvature.

problem Deriving a sharp Sobolev inequality for Riemannian manifolds with bounded Ricci curvature.
method Reduction to functions with small volume support, first order uniform asymptotic expansion of isoperimetric profile, local uniform Sobolev inequality.
result Sharp Sobolev inequality for W1,p(M)W^{1,p}(M) into Lnpnp(M)L^{\frac{np}{n-p}}(M) is derived.