Abstract:
In the second talk, I will come back the motiving examples in greater detail, notably pathwise stochastic control and conditional mean-field analysis and discuss in detail how to adapt general RSDE tools to these problems. From an interacting particle perspective, we make full use of the martingale structure of idiosyncratic noise, while no specification of the law of the (rough) common noise is required. The theory naturally comes with quantitative estimates depending on all data.
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.