scholarly journals Itô formula and Girsanov theorem for anticipating stochastic integrals

2013 ◽  
Vol 7 (3) ◽  
Author(s):  
Hui-Hsiung Kuo ◽  
Yun Peng
2002 ◽  
Vol 188 (1) ◽  
pp. 292-315 ◽  
Author(s):  
Michael Anshelevich

2013 ◽  
Vol 123 (8) ◽  
pp. 3183-3200 ◽  
Author(s):  
Paolo Da Pelo ◽  
Alberto Lanconelli ◽  
Aurel I. Stan

Author(s):  
Tomas Björk

We introduce the Wiener process, the Itô stochastic integral, and derive the Itô formula. The connection with martingale theory is discussed, and there are several worked-out examples


2016 ◽  
Vol 16 (04) ◽  
pp. 1650010 ◽  
Author(s):  
Harald Oberhauser

Dupire [16] introduced a notion of smoothness for functionals of paths and arrived at a generalization of Itō’s formula that applies to functionals with a continuous dependence on the trajectories of the underlying process. In this paper, we study nonlinear functionals that do not have such continuity. By revisiting old work of Bichteler and Karandikar we show that one can construct pathwise versions of complex functionals like the quadratic variation, stochastic integrals or Itō processes that are still regular enough such that a functional Itō-formula applies.


2021 ◽  
Author(s):  
István Gyöngy ◽  
Sizhou Wu

AbstractA well-known Itô formula for finite-dimensional processes, given in terms of stochastic integrals with respect to Wiener processes and Poisson random measures, is revisited and is revised. The revised formula, which corresponds to the classical Itô formula for semimartingales with jumps, is then used to obtain a generalisation of an important infinite-dimensional Itô formula for continuous semimartingales from Krylov (Probab Theory Relat Fields 147:583–605, 2010) to a class of $$L_p$$ L p -valued jump processes. This generalisation is motivated by applications in the theory of stochastic PDEs.


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