Add to ORKGAbstract. We discuss methods for time-discretization and simulation of square-root SDEs, both in isolation (CIR process) and as part of vector-SDEs model-ing stochastic volatility (Heston model). Both exact and biased discretization methods are covered. 1
Stochastic differential equations SDEs are used to model continuous time phenomena appearing in many...
This thesis consists of four papers A, B, C and D. Paper A and B treats the simulation of stochastic...
A commonly used approach to analyzing stochastic differential equations (SDEs) relies on performing ...
When using an Euler discretisation to simulate a mean-reverting square root process, one runs into t...
We look at numerical methods for simulation of stochastic differential equations exhibiting volatili...
The mean-reverting square root process is a stochastic differential equation (SDE) that has found co...
In the paper the simulation of stochastic processes is considered. For this purpose the estimation f...
Stochastic volatility models are increasingly important in practical derivatives pricing application...
We develop some simple simulation algorithms for CIR and Wishart processes. We investigate rigorousl...
We deal with several efficient discretization methods for the simulation of the Heston stochastic vo...
We deal with discretization schemes for the simulation of the Heston stochastic volatility model. Th...
With splitting technique, a new semi-analytical scheme with predictable strong convergence order 1.0...
We deal with discretization schemes for the simulation of the Heston stochastic volatility model. Th...
The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allow...
The Doss-Sussmann (DS) approach is used for uniform simulation of the Cox-Ingersoll-Ross (CIR) proce...
Stochastic differential equations SDEs are used to model continuous time phenomena appearing in many...
This thesis consists of four papers A, B, C and D. Paper A and B treats the simulation of stochastic...
A commonly used approach to analyzing stochastic differential equations (SDEs) relies on performing ...
When using an Euler discretisation to simulate a mean-reverting square root process, one runs into t...
We look at numerical methods for simulation of stochastic differential equations exhibiting volatili...
The mean-reverting square root process is a stochastic differential equation (SDE) that has found co...
In the paper the simulation of stochastic processes is considered. For this purpose the estimation f...
Stochastic volatility models are increasingly important in practical derivatives pricing application...
We develop some simple simulation algorithms for CIR and Wishart processes. We investigate rigorousl...
We deal with several efficient discretization methods for the simulation of the Heston stochastic vo...
We deal with discretization schemes for the simulation of the Heston stochastic volatility model. Th...
With splitting technique, a new semi-analytical scheme with predictable strong convergence order 1.0...
We deal with discretization schemes for the simulation of the Heston stochastic volatility model. Th...
The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allow...
The Doss-Sussmann (DS) approach is used for uniform simulation of the Cox-Ingersoll-Ross (CIR) proce...
Stochastic differential equations SDEs are used to model continuous time phenomena appearing in many...
This thesis consists of four papers A, B, C and D. Paper A and B treats the simulation of stochastic...
A commonly used approach to analyzing stochastic differential equations (SDEs) relies on performing ...