Stochastic Taylor Methods for Stochastic Delay Differential Equations by Norhayati, Rosli, Arifah, Bahar, Hoe, Yeak Su, Haliza, Abdul Rahman

“…This paper demonstrates a systematic derivation of high order numerical methods from stochastic Taylor expansion for solving stochastic delay differential equations (SDDEs) with a constant time lag, r > 0 . …”
Get full text

Derivation of stochastic Taylor methods for stochastic differential equations by Noor Amalina Nisa, Ariffin, Norhayati, Rosli

“…This paper demonstrates a derivation of stochastic Taylor methods for stochastic differential equations (SDEs). …”
Get full text

Stochastic quantization

Get full text

Stochastic Volatility. by Shephard, N

“…Stochastic volatility (SV) is the main concept used in the elds of nancial economics and mathematical nance to deal with the endemic time-varying volatility and codependence found in nancial markets. …”

Stochastic Runge-Kutta method for stochastic delay differential equations by Norhayati, Rosli

“…Random effect and time delay are inherent properties of many real phenomena around us, hence it is required to model the system via stochastic delay differential equations(SDDEs). However,the complexity arises due to the presence of both randomness and time delay.The analytical solution of SDDEs is hard to be found.In such a case, a numerical method provides a way to solve the problem.Nevertheless, due to the lacking of numerical methods available for solving.SDDEs,a wide range of researchers among the mathematicians and scientists have not incorporated the important features of the real phenomena,which include randomness and time delay in modeling the system.Hence,this research aims to generalize the convergence proof of numerical methods for SDDEs when the drift and diffusion functions are Taylor expansion and to develop a stochastic Runge—Kutta for solving SDDEs Motivated by the relative paucity of numerical methods accessible in simulating the strong solution of SDDEs,the numerical schemes developed in this research is hoped to bridge the gap between the evolution of numerical methods in ordinary differential equations(ODEs), delay differential equations (DDEs),stochastic differential equations(SDEs)and SDDEs.The extension of numerical methods of SDDEs is far from complete.Rate of convergence of recent numerical methods available in approximating the solution of SDDEs only reached the order of 1.0. …”
Get full text

Stochastic evolution equations for large portfolios of stochastic volatility models by Hambly, B, Kolliopoulos, N

“…We consider a large market model of defaultable assets in which the asset price processes are modelled as Heston-type stochastic volatility models with default upon hitting a lower boundary. …”

Fifth-stage stochastic runge-kutta method for stochastic differential equations by Noor Amalina Nisa, Ariffin

“…Hence, models for these systems are required via stochastic differential equations (SDEs). However, it is often difficult to find analytical solutions of SDEs. …”
Get full text

2-stage Stochastic Runge-Kutta for Stochastic Delay Differential Equations by Norhayati, Rosli, Arifah, Bahar, S. H., Yeak, Rahimah, Jusoh

“…This paper proposes a newly developed one-step derivative-free method, that is 2-stage stochastic Runge-Kutta (SRK2) to approximate the solution of stochastic delay differential equations (SDDEs) with a constant time lag, r 0. …”
Get full text

2–stage Stochastic Runge–Kutta for Stochastic Delay Differential Equations by Norhayati, Rosli, Arifah, Bahar, S. H., Yeak, Rahimah, Jusoh

“…This paper proposes a newly developed one-step derivative-free method, that is 2-stage stochastic Runge-Kutta (SRK2) to approximate the solution of stochastic delay differential equations (SDDEs) with a constant time lag, r > 0. …”
Get full text

Analytical optimal control theory as applied to stochastic and non-stochastic economics. by Merton, Robert C

Get full text

Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus by Breton, Jean-Christophe, Privault, Nicolas

Subjects: Get full text

Convergence of stochastic nonlinear systems and implications for Stochastic Model Predictive Control by Muñoz-Carpintero, D, Cannon, M

“…We discuss implications for the convergence of the state and control laws of stochastic MPC formulations, and we prove convergence results for several existing stochastic MPC formulations for linear and nonlinear systems.…”

Pricing variance swaps under stochastic volatility and stochastic interest rate by Cao, Jiling, Lian, Guanghua, Roslan, Teh Raihana Nazirah

“…In this paper, we investigate the effects of imposing stochastic interest rate driven by the Cox–Ingersoll–Ross process along with the Heston stochastic volatility model for pricing variance swaps with discrete sampling times. …”
Get full text

Stochastic wasserstein barycenters by Solomon, Justin, Chien, Edward, Claici, Sebastian

“…All rights reserved. Wi present a stochastic algorithm to compute the baryccntcr of a set of probability distributions under the Wasscrstcin metric from optimal transport Unlike previous approaches,our method extends to continuous input distributions and allows the support of the baryccntcr to be adjusted in each iteration. …”
Get full text

Delayed stochastic mapping by Rikoski, Richard J. (Richard James), 1976-

Get full text

Stochastic boosting algorithms by Jasra, A, Holmes, C

MPC for Stochastic systems by Cannon, M, Couchman, P, Kouvaritakis, B

“…Stochastic uncertainty is present in many control engineering problems, and is also present in a wider class of applications, such as finance and sustainable development. …”

Stochastic spread of Wolbachia. by Jansen, V, Turelli, M, Godfray, H

“…The significance of stochastic effects in the natural spread of Wolbachia and their relevance to the use of Wolbachia as a drive mechanism in vector and pest management are discussed.…”

Stochastic boosting algorithms by Jasra, A, Holmes, C

“…In this article we develop a class of stochastic boosting (SB) algorithms, which build upon the work of Holmes and Pintore (Bayesian Stat. 8, Oxford University Press, Oxford, 2007). …”

The Supermodular Stochastic Ordering by Meyer, M, Strulovici, B

“…One multivariate distribution dominates another according to the supermodular stochastic ordering if it yields a higher expectation than the other for all supermodular objective functions. …”