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We consider singularly perturbed convection-diffusion equations on one-dimensional networks (metric graphs) as well as the transport problems arising in the vanishing diffusion limit. Suitable coupling condition at inner vertices are…

Analysis of PDEs · Mathematics 2020-04-22 Herbert Egger , Nora Philippi

In this paper we discuss the basket options valuation for a jump-diffusion model. The underlying asset prices follow some correlated local volatility diffusion processes with systematic jumps. We derive a forward partial integral…

Computational Finance · Quantitative Finance 2010-03-10 Guoping Xu , Harry Zheng

Probabilistic graphical models are a powerful concept for modeling high-dimensional distributions. Besides modeling distributions, probabilistic graphical models also provide an elegant framework for performing statistical inference;…

Artificial Intelligence · Computer Science 2022-09-13 Christian Knoll

Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the…

Statistics Theory · Mathematics 2015-04-03 Xin-Bing Kong , Zhi Liu , Bing-Yi Jing

In the present work, we explore homogenization techniques for a class of switching diffusion processes whose drift and diffusion coefficients, and jump intensities are smooth, spatially periodic functions; we assume full coupling between…

Probability · Mathematics 2025-07-01 Chetan D. Pahlajani

The value function of an optimal stopping problem for jump diffusions is known to be a generalized solution of a variational inequality. Assuming that the diffusion component of the process is nondegenerate and a mild assumption on the…

Optimization and Control · Mathematics 2012-03-16 Erhan Bayraktar , Hao Xing

Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges on the fact that the underlying physical phenomena are…

Traditional turbulence models are derived for single-phase flow. Extension of the family of two-equation turbulence models for two-phase flow is obtained via scaling the transport equations by the density. In the special case of two-phase…

Fluid Dynamics · Physics 2023-11-28 Omar Elsayed , Benjamin Bouscasse , Maité Gouin , David Le Touzé

We study the homogenization of a steady diffusion equation in a highly heterogeneous medium made of two subregions separated by a periodic barrier through which the flow is proportional to the jump of the temperature by a layer conductance…

Analysis of PDEs · Mathematics 2008-11-08 Abdelhamid Ainouz

A cut-and-paste model which mimics a trial-and-error process of adaptation is introduced and solved. The model, which can be thought of as a diffusion process with memory, is characterized by two properties, efficiency and persistence. We…

Statistical Mechanics · Physics 2007-05-23 R. D'Hulst , G. J. Rodgers

We develop a general framework for finding error estimates for convection-diffusion equations with nonlocal, nonlinear, and possibly degenerate diffusion terms. The equations are nonlocal because they involve fractional diffusion operators…

Analysis of PDEs · Mathematics 2013-10-08 Nathaël Alibaud , Simone Cifani , Espen R. Jakobsen

In this paper, a pricing formula for volatility swaps is delivered when the underlying asset follows the stochastic volatility model with jumps and stochastic intensity. By using Feynman-Kac theorem, a partial integral differential equation…

Pricing of Securities · Quantitative Finance 2018-05-21 Ben-zhang Yang , Jia Yue , Ming-hui Wang , Nan-jing Huang

We take a new look at the problem of disentangling the volatility and jumps processes of daily stock returns. We first provide a computational framework for the univariate stochastic volatility model with Poisson-driven jumps that offers a…

Statistical Finance · Quantitative Finance 2021-04-30 Angelos Alexopoulos , Petros Dellaportas , Omiros Papaspiliopoulos

In this paper, we are presenting a method for estimation of market parameters modeled by jump diffusion process. The method proposed is based on Gibbs sampler, while the market parameters are the drift, the volatility, the jump intensity…

Pricing of Securities · Quantitative Finance 2017-12-22 Kein Joe Lau , Yong Kheng Goh , An-Chow Lai

We consider the problem of estimating a vector of unknown constant parameters for a class of hybrid dynamical systems -- that is, systems whose state variables exhibit both continuous (flow) and discrete (jump) evolution. Using a hybrid…

Optimization and Control · Mathematics 2023-08-16 Ryan S. Johnson , Stefano Di Cairano , Ricardo G. Sanfelice

We prove that the perpetual American put option price of level dependent volatility model with compound Poisson jumps is convex and is the classical solution of its associated quasi-variational inequality, that it is $C^2$ except at the…

Optimization and Control · Mathematics 2009-01-21 Erhan Bayraktar

We consider a drift-diffusion model, with an unknown function depending on the spatial variable and an additional structural variable, the amount of ingested lipid. The diffusion coefficient depends on this additional variable. The drift…

Analysis of PDEs · Mathematics 2023-05-10 Cosmin Burtea , Nicolas Meunier , Clément Mouhot

In this short paper, in order to price occupation-time options, such as (double-barrier) step options and quantile options, we derive various joint distributions of a mixed-exponential jump-diffusion process and its occupation times of…

Probability · Mathematics 2016-03-31 Djilali Ait Aoudia , Jean-François Renaud

We study a two-species competition model in a patchy advective environment, where the species are subject to both directional drift and undirectional random dispersal between patches and there are losses of individuals in the downstream end…

Dynamical Systems · Mathematics 2023-03-22 Shanshan Chen , Junping Shi , Zhisheng Shuai , Yixiang Wu

Diffusion models are a class of probabilistic generative models that have been widely used as a prior for image processing tasks like text conditional generation and inpainting. We demonstrate that these models can be adapted to make…

Machine Learning · Computer Science 2023-06-14 Marc Finzi , Anudhyan Boral , Andrew Gordon Wilson , Fei Sha , Leonardo Zepeda-Núñez