Positivity-preserving numerical scheme for the alpha-constant elasticity of variance process

In this article, we present a method for constructing a positivity-preserving numerical scheme for a jump-extended constant elasticity of variance (CEV) process, where jumps are governed by a spectrally positive α -stable process with α ∈ ( 1 , 2 ) . The numerical scheme is obtained by making the diffusion coefficient x γ , where γ ∈ ( 1 2 , 1 ) , partially implicit and then finding the appropriate adjustment factor. We show that for a sufficiently small step size, the proposed scheme converges and theoretically achieves a strong convergence rate of at least 1 2 ( α − 2 ∧ 1 α ∧ ρ ) , where ρ ∈ ( 1 2 , 1 ) is the Hölder exponent of the jump coefficient x ρ and the constant α − < α can be chosen as arbitrarily close to α ∈ ( 1 , 2 ) .

Authors

Institutions

Publication Details

Journal
Journal of Mathematical Analysis and Applications
Published
2025-02-05
DOI
https://doi.org/10.1016/j.jmaa.2025.129341
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Positivity-preserving numerical scheme for the alpha-constant elasticity of variance process

Guanting Liu, Libo Li
Journal of Mathematical Analysis and Applications
Stochastic processes and financial applications
article

Positivity-preserving numerical scheme for the alpha-constant elasticity of variance process

Guanting Liu, Libo Li
article en

Abstract

In this article, we present a method for constructing a positivity-preserving numerical scheme for a jump-extended constant elasticity of variance (CEV) process, where jumps are governed by a spectrally positive α -stable process with α ∈ ( 1 , 2 ) . The numerical scheme is obtained by making the diffusion coefficient x γ , where γ ∈ ( 1 2 , 1 ) , partially implicit and then finding the appropriate adjustment factor. We show that for a sufficiently small step size, the proposed scheme converges and theoretically achieves a strong convergence rate of at least 1 2 ( α − 2 ∧ 1 α ∧ ρ ) , where ρ ∈ ( 1 2 , 1 ) is the Hölder exponent of the jump coefficient x ρ and the constant α − < α can be chosen as arbitrarily close to α ∈ ( 1 , 2 ) .

Journal of Mathematical Analysis and ApplicationsVol. 547(2)
UNSW Sydney (AU)
Reduced inequalities
Openalex Percentile: Top 97%
Stochastic processes and financial applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.