Abstract:
Rough stochastic differential equations (RSDEs) are common generalisations of Itō SDEs and Lyons RDEs. Since their introduction in 2021 (Hocquet-Lê-F) they have emerged as a powerful tool in several areas of applied probability, including non-linear stochastic filtering, pathwise stochastic optimal control, volatility modelling in finance and mean-field analysis conditional on common noise. This talk will offer an overview of motivating examples, including some classes of stochastic partial differential equations, before presenting the basic ideas of the theory, starting with a primer on classical rough differential equations.
Biography:
Peter K. Friz is a mathematician working in the fields of partial differential equations, quantitative finance, and stochastic analysis. He studied at the Vienna University of Technology, Ecole Centrale Paris, University of Cambridge and Courant Institute of Mathematical Sciences, New York University, and obtained his PhD in 2004 under the supervision of S. R. Srinivasa Varadhan. He worked as a quantitative associate at Merrill Lynch, then held academic positions at the University of Cambridge, and the Radon Institute. Since 2009, he is full professor at Technische Universität Berlin, and also affiliated to the Weierstrass Institute for Applied Analysis and Stochastics in Berlin.
Seminar by the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai
This event is open to the NYU Shanghai community and Math community.