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Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion

Tubikanec, Irene ; Tamborrino, Massimiliano ; Lansky, Petr and Buckwar, Evelyn LU (2022) In Journal of Computational and Applied Mathematics 406.
Abstract

We provide a comparative analysis of qualitative features of different numerical methods for the inhomogeneous geometric Brownian motion (IGBM). The limit distribution of the IGBM exists, its conditional and asymptotic mean and variance are known and the process can be characterised according to Feller's boundary classification. We compare the frequently used Euler–Maruyama and Milstein methods, two Lie–Trotter and two Strang splitting schemes and two methods based on the ordinary differential equation (ODE) approach, namely the classical Wong–Zakai approximation and the recently proposed log-ODE scheme. First, we prove that, in contrast to the Euler–Maruyama and Milstein schemes, the splitting and ODE schemes preserve the boundary... (More)

We provide a comparative analysis of qualitative features of different numerical methods for the inhomogeneous geometric Brownian motion (IGBM). The limit distribution of the IGBM exists, its conditional and asymptotic mean and variance are known and the process can be characterised according to Feller's boundary classification. We compare the frequently used Euler–Maruyama and Milstein methods, two Lie–Trotter and two Strang splitting schemes and two methods based on the ordinary differential equation (ODE) approach, namely the classical Wong–Zakai approximation and the recently proposed log-ODE scheme. First, we prove that, in contrast to the Euler–Maruyama and Milstein schemes, the splitting and ODE schemes preserve the boundary properties of the process, independently of the choice of the time discretisation step. Second, we prove that the limit distribution of the splitting and ODE methods exists for all stepsize values and parameters. Third, we derive closed-form expressions for the conditional and asymptotic means and variances of all considered schemes and analyse the resulting biases. While the Euler–Maruyama and Milstein schemes are the only methods which may have an asymptotically unbiased mean, the splitting and ODE schemes perform better in terms of variance preservation. The Strang schemes outperform the Lie–Trotter splittings, and the log-ODE scheme the classical ODE method. The mean and variance biases of the log-ODE scheme are very small for many relevant parameter settings. However, in some situations the two derived Strang splittings may be a better alternative, one of them requiring considerably less computational effort than the log-ODE method. The proposed analysis may be carried out in a similar fashion on other numerical methods and stochastic differential equations with comparable features.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Boundary preservation, Feller's boundary classification, GARCH model, Log-ODE method, Moment preservation, Numerical splitting schemes
in
Journal of Computational and Applied Mathematics
volume
406
article number
113951
publisher
Elsevier
external identifiers
  • scopus:85121879507
ISSN
0377-0427
DOI
10.1016/j.cam.2021.113951
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2021
id
b46ff588-c5a4-4799-b872-73a75967ee7a
date added to LUP
2022-02-21 15:23:08
date last changed
2022-04-24 06:13:56
@article{b46ff588-c5a4-4799-b872-73a75967ee7a,
  abstract     = {{<p>We provide a comparative analysis of qualitative features of different numerical methods for the inhomogeneous geometric Brownian motion (IGBM). The limit distribution of the IGBM exists, its conditional and asymptotic mean and variance are known and the process can be characterised according to Feller's boundary classification. We compare the frequently used Euler–Maruyama and Milstein methods, two Lie–Trotter and two Strang splitting schemes and two methods based on the ordinary differential equation (ODE) approach, namely the classical Wong–Zakai approximation and the recently proposed log-ODE scheme. First, we prove that, in contrast to the Euler–Maruyama and Milstein schemes, the splitting and ODE schemes preserve the boundary properties of the process, independently of the choice of the time discretisation step. Second, we prove that the limit distribution of the splitting and ODE methods exists for all stepsize values and parameters. Third, we derive closed-form expressions for the conditional and asymptotic means and variances of all considered schemes and analyse the resulting biases. While the Euler–Maruyama and Milstein schemes are the only methods which may have an asymptotically unbiased mean, the splitting and ODE schemes perform better in terms of variance preservation. The Strang schemes outperform the Lie–Trotter splittings, and the log-ODE scheme the classical ODE method. The mean and variance biases of the log-ODE scheme are very small for many relevant parameter settings. However, in some situations the two derived Strang splittings may be a better alternative, one of them requiring considerably less computational effort than the log-ODE method. The proposed analysis may be carried out in a similar fashion on other numerical methods and stochastic differential equations with comparable features.</p>}},
  author       = {{Tubikanec, Irene and Tamborrino, Massimiliano and Lansky, Petr and Buckwar, Evelyn}},
  issn         = {{0377-0427}},
  keywords     = {{Boundary preservation; Feller's boundary classification; GARCH model; Log-ODE method; Moment preservation; Numerical splitting schemes}},
  language     = {{eng}},
  month        = {{05}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Computational and Applied Mathematics}},
  title        = {{Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion}},
  url          = {{http://dx.doi.org/10.1016/j.cam.2021.113951}},
  doi          = {{10.1016/j.cam.2021.113951}},
  volume       = {{406}},
  year         = {{2022}},
}