Stochastic calculus via regularizations
Author(s)
Bibliographic Information
Stochastic calculus via regularizations
（Bocconi & Springer series / (series editors) Sandro Salsa ... [et al.], 11）
Springer, c2022
- :hbk.
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Note
Includes bibliographical reference (p.617-631) and index
Description and Table of Contents
Description
The book constitutes an introduction to stochastic calculus, stochastic differential equations and related topics such as Malliavin calculus. On the other hand it focuses on the techniques of stochastic integration and calculus via regularization initiated by the authors. The definitions relies on a smoothing procedure of the integrator process, they generalize the usual Ito and Stratonovich integrals for Brownian motion but the integrator could also not be a semimartingale and the integrand is allowed to be anticipating. The resulting calculus requires a simple formalism: nevertheless it entails pathwise techniques even though it takes into account randomness. It allows connecting different types of pathwise and non pathwise integrals such as Young, fractional, Skorohod integrals, enlargement of filtration and rough paths. The covariation, but also high order variations, play a fundamental role in the calculus via regularization, which can also be applied for irregular integrators. A large class of Gaussian processes, various generalizations of semimartingales such that Dirichlet and weak Dirichlet processes are revisited. Stochastic calculus via regularization has been successfully used in applications, for instance in robust finance and on modeling vortex filaments in turbulence. The book is addressed to PhD students and researchers in stochastic analysis and applications to various fields.
Table of Contents
- 1. Review on Basic Probability Theory. - 2. Processes, Brownian Motion and Martingales. - 3. Fractional Brownian Motion and Related Processes. - 4. Stochastic Integration via Regularization. - 5. Ito Integrals. - 6. Stability of the Covariation and Ito's Formula. - 7. Change of probability and martingale representation. - 8. About finite quadratic variation: examples. - 9. Hermite Polynomials and Wiener Chaos. - 10. Elements of Wiener Analysis. - 11. Elements of Non-causal Calculus. - 12. Ito Classical Stochastic Differential Equations. - 13. Ito SDEs with Non-Lipschitz Coefficients. - 14. Foellmer-Dirichlet Processes. - 15. Weak Dirichlet Processes. - Stochastic Calculus with n-Covariations. - Calculus via Regularization and Rough Paths.
by "Nielsen BookData"