Convergence of a discretization scheme for jump-diffusion processes with state–dependent intensities

By Paul Glasserman, Nicolas Merener. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 2004

61 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Finance, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/f14a0f7decdc5006c67125e8cdfe1360729c9def

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Nicola Bruti-Liberati
2. Desmond John Higham
3. George O. Mohler
4. Peter E. Kloeden
5. P. Jeffrey Brantingham
6. Eckhard Platen
7. George E. Tita
8. Martin B. Short

In Finance

1. Peter E. Kloeden
2. Nicola Bruti-Liberati
3. Desmond John Higham
4. George O. Mohler
5. P. Jeffrey Brantingham
6. Eckhard Platen
7. George E. Tita
8. Martin B. Short

In Modeling and Simulation

1. Desmond John Higham
2. George O. Mohler
3. Peter E. Kloeden
4. P. Jeffrey Brantingham
5. George E. Tita
6. Martin B. Short

Citations in the corpus

• CONVERGENCE AND STABILITY OF IMPLICIT METHODS FOR JUMP-DIFFUSION SYSTEMS (2005, methodology): “A-stability, backward Euler, Euler–Maruyama, linear stabil- ity, Poisson process, stochastic differential equation, strong convergence, theta method, trapezoidal rule.”
• Gang rivalry dynamics via coupled point process networks (2014, methodology): “In this case a standard numerical method for the approximation of deterministic or stochastic differential equations is used to evolve the state variable between events [4].”
• Strong approximations of stochastic differential equations with jumps (2007, background): “For weak approximations of SDEs with jumps we refer to [28,24,21,10,3].”