Malliavin calculus and stochastic integrals

1986 ◽  
pp. 182-194 ◽  
Author(s):  
David Nualart
Keyword(s):  
Malliavin Calculus ◽  
Stochastic Integrals

scholarly journals MULTIDIMENSIONAL BIFRACTIONAL BROWNIAN MOTION: ITÔ AND TANAKA FORMULAS

2007 ◽  
Vol 07 (03) ◽  
pp. 365-388 ◽  
Author(s):  
KHALIFA ES-SEBAIY ◽  
CIPRIAN A. TUDOR
Keyword(s):  
Brownian Motion ◽  
Gaussian Processes ◽  
Malliavin Calculus ◽  
Stochastic Integrals ◽  
Bifractional Brownian Motion ◽  
Multiple Stochastic Integrals

Using the Malliavin calculus with respect to Gaussian processes and the multiple stochastic integrals, we derive Itô's and Tanaka's formulas for the d-dimensional bifractional Brownian motion.

Generalized stochastic integrals and the malliavin calculus

1986 ◽  
Vol 73 (2) ◽  
pp. 255-280 ◽  
Author(s):  
David Nualart ◽  
Moshe Zakai
Keyword(s):  
Malliavin Calculus ◽  
Stochastic Integrals

scholarly journals Weak convergence of stochastic integrals with respect to the state occupation measure of a Markov chain

2021 ◽  
Vol 58 (2) ◽  
pp. 372-393
Author(s):  
H. M. Jansen
Keyword(s):  
Markov Chain ◽  
Weak Convergence ◽  
Regime Switching ◽  
Sufficient Conditions ◽  
The State ◽  
Stochastic Integrals ◽  
Occupation Measure ◽  
Generator Matrix ◽  
Markov Modulated ◽  
Erlang Loss

AbstractOur aim is to find sufficient conditions for weak convergence of stochastic integrals with respect to the state occupation measure of a Markov chain. First, we study properties of the state indicator function and the state occupation measure of a Markov chain. In particular, we establish weak convergence of the state occupation measure under a scaling of the generator matrix. Then, relying on the connection between the state occupation measure and the Dynkin martingale, we provide sufficient conditions for weak convergence of stochastic integrals with respect to the state occupation measure. We apply our results to derive diffusion limits for the Markov-modulated Erlang loss model and the regime-switching Cox–Ingersoll–Ross process.

On Krylov’s estimates for optional semimartingales

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
M. Abdelghani ◽  
A. Melnikov ◽  
A. Pak
Keyword(s):  
Sobolev Space ◽  
Measurable Function ◽  
Diffusion Processes ◽  
Stochastic Integrals ◽  
Controlled Diffusion ◽  
Convergence Of Solutions ◽  
Change Of Variables ◽  
General Class Of Functions ◽  
Class Of Functions ◽  
Controlled Diffusion Processes

Abstract The estimates of N. V. Krylov for distributions of stochastic integrals by means of the L d {L_{d}} -norm of a measurable function are well-known and are widely used in the theory of stochastic differential equations and controlled diffusion processes. We generalize estimates of this type for optional semimartingales, then apply these estimates to prove the change of variables formula for a general class of functions from the Sobolev space W d 2 {W^{2}_{d}} . We also show how to use these estimates for the investigation of L 2 {L^{2}} -convergence of solutions of optional SDE’s.

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