Pathwise comparison of solutions to a class of stochastic systems of differential equations is proved which extends the existing result of Geiß and Manthey. When the diffusion coefficients are different, the Gal\u27čhuk-Davis method is extended to establish the comparison results. We illustrate our results with several examples some of which arise in stochastic finance theory
For some backward stochastic Volterra integral equations (BSVIEs) in multi-dimensional Euclidean spa...
A useful result when dealing with backward stochastic differential equations is the comparison theor...
AbstractThis paper is a continuation of our previous work (Part I, Stochastic Process. Appl. 93 (200...
We prove comparison theorems for systems of ordinary stochastic differential equations as well as fo...
AbstractWe prove comparison theorems for systems of ordinary stochastic differential equations as we...
AbstractThe problem of non-confluence and strong comparison of solutions of one-dimensional Itô stoc...
We consider a system of stochastic differential equations driven by a standard n-dimensional Browni...
AbstractWe provide a general comparison theorem for systems of stochastic partial differential equat...
AbstractIn this paper, we present a new approach to obtain the comparison theorem of two 1-dimension...
AbstractBy the local time method we prove comparison theorems for systems of stochastic differential...
Main results for stochastic differential equations apply to their stationary solutions. For obvious ...
By the local time method we prove comparison theorems for systems of stochastic differential inequal...
We consider the Stochastic Differential Equation $X_t = X_0 + \int_0^t b(s,X_s) ds + B_t$, in $\math...
For some backward stochastic Volterra integral equations (BSVIEs) in multi-dimensional Euclidean spa...
Comparison theorems for solutions of one-dimensional backward stochastic differential equations were...
For some backward stochastic Volterra integral equations (BSVIEs) in multi-dimensional Euclidean spa...
A useful result when dealing with backward stochastic differential equations is the comparison theor...
AbstractThis paper is a continuation of our previous work (Part I, Stochastic Process. Appl. 93 (200...
We prove comparison theorems for systems of ordinary stochastic differential equations as well as fo...
AbstractWe prove comparison theorems for systems of ordinary stochastic differential equations as we...
AbstractThe problem of non-confluence and strong comparison of solutions of one-dimensional Itô stoc...
We consider a system of stochastic differential equations driven by a standard n-dimensional Browni...
AbstractWe provide a general comparison theorem for systems of stochastic partial differential equat...
AbstractIn this paper, we present a new approach to obtain the comparison theorem of two 1-dimension...
AbstractBy the local time method we prove comparison theorems for systems of stochastic differential...
Main results for stochastic differential equations apply to their stationary solutions. For obvious ...
By the local time method we prove comparison theorems for systems of stochastic differential inequal...
We consider the Stochastic Differential Equation $X_t = X_0 + \int_0^t b(s,X_s) ds + B_t$, in $\math...
For some backward stochastic Volterra integral equations (BSVIEs) in multi-dimensional Euclidean spa...
Comparison theorems for solutions of one-dimensional backward stochastic differential equations were...
For some backward stochastic Volterra integral equations (BSVIEs) in multi-dimensional Euclidean spa...
A useful result when dealing with backward stochastic differential equations is the comparison theor...
AbstractThis paper is a continuation of our previous work (Part I, Stochastic Process. Appl. 93 (200...