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We determine the variance-optimal hedge when the logarithm of the underlying price follows a process with stationary independent increments in discrete or continuous time. Although the general solution to this problem is known as backward…

Probability · Mathematics 2008-12-10 Friedrich Hubalek , Jan Kallsen , Leszek Krawczyk

We propose a versatile Monte-Carlo method for pricing and hedging options when the market is incomplete, for an arbitrary risk criterion (chosen here to be the expected shortfall), for a large class of stochastic processes, and in the…

Condensed Matter · Physics 2007-05-23 Benoît Pochart , Jean-Philippe Bouchaud

Building upon the recent work of Teso and Plociniczak (2025) regarding L1 discretization errors for the Caputo derivative in H\"{o}lder spaces, this study extends the analysis to higher-order discretization errors within the same functional…

Numerical Analysis · Mathematics 2025-04-11 Xiangyi Peng , Lisen Ding , Dongling Wang

In this paper, we propose a method, that is based on equivariant moving frames, for development of high order accurate invariant compact finite difference schemes that preserve Lie symmetries of underlying partial differential equations. In…

Mathematical Physics · Physics 2020-02-19 Ersin Ozbenli , Prakash Vedula

The derivation of combined prefactored compact schemes for first and second order derivatives is described here, relying on the Fourier analysis of the original prefactored compact schemes. By this approach, the order of accuracy of the…

Numerical Analysis · Mathematics 2019-02-13 Adrian Sescu

In this article, a high-order time-stepping scheme based on the cubic interpolation formula is considered to approximate the generalized Caputo fractional derivative (GCFD). Convergence order for this scheme is $(4-\alpha)$, where $\alpha…

Numerical Analysis · Mathematics 2022-10-12 Sarita Kumari , Rajesh K. Pandey , R. P. Agarwal

Finite-difference methods are widely used for zeroth-order optimization in settings where gradient information is unavailable or expensive to compute. These procedures mimic first-order strategies by approximating gradients through function…

Optimization and Control · Mathematics 2025-05-27 Marco Rando , Cesare Molinari , Lorenzo Rosasco , Silvia Villa

A general method for accelerating fixed point schemes for problems related to partial differential equations is presented in this article. The speedup is obtained by training a reduced-order model on-the-fly, removing the need to do an…

Numerical Analysis · Mathematics 2025-12-01 Philippe-André Luneau , Jean Deteix

Finite-sum optimization problems are ubiquitous in machine learning, and are commonly solved using first-order methods which rely on gradient computations. Recently, there has been growing interest in \emph{second-order} methods, which rely…

Optimization and Control · Mathematics 2017-03-09 Yossi Arjevani , Ohad Shamir

We consider rate swaps which pay a fixed rate against a floating rate in presence of bid-ask spread costs. Even for simple models of bid-ask spread costs, there is no explicit strategy optimizing an expected function of the hedging error.…

Computational Finance · Quantitative Finance 2016-04-13 Christophe Michel , Victor Reutenauer , Denis Talay , Etienne Tanré

This article addresses the research question if and how the finite cell method, an embedded domain finite element method of high order, may be used in the simulation of metal deposition to harvest its computational efficiency. This…

Numerical Analysis · Mathematics 2018-09-26 Ali Özcan , Stefan Kollmannsberger , John N. Jomo , Ernst Rank

We study indifference pricing of exotic derivatives by using hedging strategies that take static positions in quoted derivatives but trade the underlying and cash dynamically over time. We use real quotes that come with bid-ask spreads and…

Pricing of Securities · Quantitative Finance 2020-08-05 Teemu Pennanen , Udomsak Rakwongwan

We present a multigrid iterative algorithm for solving a system of coupled free boundary problems for pricing American put options with regime-switching. The algorithm is based on our recently developed compact finite difference scheme…

Computational Finance · Quantitative Finance 2021-11-09 Chinonso Nwankwo , Weizhong Dai

In this paper, we study the behavior of the Hedge algorithm in the online stochastic setting. We prove that anytime Hedge with decreasing learning rate, which is one of the simplest algorithm for the problem of prediction with expert…

Machine Learning · Statistics 2019-07-10 Jaouad Mourtada , Stéphane Gaïffas

We consider the discretized Bachelier model where hedging is done on an equidistant set of times. Exponential utility indifference prices are studied for path-dependent European options and we compute their non-trivial scaling limit for a…

Probability · Mathematics 2022-03-03 Asaf Cohen , Yan Dolinsky

Model-based process simulation can be used to derive designs and operating conditions of chemical processes that optimally balance multiple objectives, such as quality, costs, or environmental impacts. This work focuses on identifying…

We consider a general path-dependent version of the hedging problem with price impact of Bouchard et al. (2019), in which a dual formulation for the super-hedging price is obtained by means of PDE arguments, in a Markovian setting and under…

Probability · Mathematics 2020-01-09 Bruno Bouchard , Xiaolu Tan

The new class of alternating-conjugate splitting methods is presented and analyzed. They are obtained by concatenating a given composition involving complex coefficients with the same composition but with the complex conjugate coefficients.…

Numerical Analysis · Mathematics 2025-12-19 J. Bernier , S. Blanes , F. Casas , A. Escorihuela-Tomàs

This study aims to construct a stable, high-order compact finite difference method for solving Sobolev-type equations with Dirichlet boundary conditions in one-space dimension. Approximation of higher-order mixed derivatives in some…

Numerical Analysis · Mathematics 2025-06-05 Lavanya V Salian , Samala Rathan , Rakesh Kumar

This paper advances the computational efficiency of Deep Hedging frameworks through the novel integration of Kronecker-Factored Approximate Curvature (K-FAC) optimization. While recent literature has established Deep Hedging as a…

Statistical Finance · Quantitative Finance 2024-11-25 Tsogt-Ochir Enkhbayar
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