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Category Archives: Stochastic Calculus lectures
Diffusion processes and stochastic calculus textbook
My book which is published by the European Mathematical Society is now available. Diffusion Processes and Stochastic Calculus The content is partially based on the lecture notes in stochastic calculus and rough paths theory which are posted on this blog … Continue reading
Lecture 35. Weak differentiability for solutions of stochastic differential equations and the existence of a smooth density
As usual, we consider a filtered probability space which satisfies the usual conditions and on which is defined a -dimensional Brownian motion . Our purpose here, is to prove that solutions of stochastic differential equations are differentiable in the sense … Continue reading
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Lecture 34. The Wiener chaos expansion
As in the previous Lectures, we consider a filtered probability space on which is defined a Brownian motion , and we assume that is the usual completion of the natural filtration of . Our goal is here to write an … Continue reading
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Lecture 33. The Malliavin matrix and existence of densities
More generally, by using the same methods as in the previous Lecture, we can introduce iterated derivatives. If , we set . We may then consider as a square integrable random process indexed by and valued in . By using … Continue reading
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Lecture 32. The Malliavin derivative
The next Lectures will be devoted to the study of the problem of the existence of a density for solutions of stochastic differential equations. The basic tool to study such questions is the so-called Malliavin calculus. Let us consider a … Continue reading
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Lecture 30. Stratonovitch stochastic differential equations
As usual, let be a filtered probability space which satisfies the usual conditions. It is often useful to use the language of Stratonovitch ‘s integration to study stochastic differential equations because the Itō’s formula takes a much nicer form. If … Continue reading
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Lecture 29. The strong Markov property for solutions of stochastic differential equations
In the previous section, we have seen that if is the solution of a stochastic differential equation then is a Markov process, that is for every , where . It is remarkable that this property still holds when is now … Continue reading
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Lecture 28. The Feynman-Kac formula
It is now time to give some applications of the theory of stochastic differential equations to parabolic second order partial differential equations. In particular we are going to prove that solutions of such equations can represented by using solutions of … Continue reading
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Lecture 27. Stochastic differential equations. Regularity of the flow
In this lecture, we study the regularity of the solution of a stochastic differential equation with respect to its initial condition. The key tool is a multimensional parameter extension of the Kolmogorov continuity theorem whose proof is almost identical to … Continue reading
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