ETH Zurich

Semidefinite programming (SDP) techniques have been extremely successful

in many practical engineering design questions. In several of these

applications, the problem structure is invariant under the action of

some symmetry group, and this property is naturally inherited by the

underlying optimization. A natural question, therefore, is how to

exploit this information for faster, better conditioned, and more

reliable algorithms. To this effect, we study the associative algebra

associated with a given SDP, and show the striking advantages of a

careful use of symmetries. The results are motivated and illustrated

through applications of SDP and sum of squares techniques from networked

control theory, analysis and design of Markov chains, and quantum

information theory.

A random planar graph $P_n$ is a graph drawn uniformly at random from the class of all (labelled) planar graphs on $n$ vertices. In this talk we show that with probability $1-o(1)$ the number of vertices of degree $k$ in $P_n$ is very close to a quantity $d_k n$ that we determine explicitly. Here $k=k(n) \le c \log n$. In the talk our main emphasis will be on the techniques for proving such results. (Joint work with Kosta Panagiotou.)

Recently, Liang, Lyons and Qian developed a new methodology for the study of backward stochastic differential equations (BSDEs) on general filtered probability spaces. Their approach is based on the analysis of a particular class of functional differential equations, where the driver of the equation does not depend only on the present, but also on the terminal value of the solution.

The purpose of this work is to study fully coupled systems of forward functional differential equations, which are related to a broad class of fully coupled forward-backward stochastic dynamics with respect to general filtrations. In particular, these systems of functional differential equations have a more homogeneous structure with respect to the underlying forward-backward problems, allowing to partly avoid the conflicting nature between the forward and backward components.

Another advantage of the approach is that its generality allows to consider many other types of forward-backward equations not treated in the classical literature: this is shown with the help of several examples, which have interesting applications to mathematical finance and are related to parabolic integro-partial differential equations. In the second part of the talk, we introduce a numerical scheme for the approximation of decoupled systems, based on a time discretization combined with a local iteration approach.

I will introduce the notion of a nonlinear Levy process, discuss basic well-posednes, SDE links and the connection with interacting particles. The talk is aimed to be an introduction to the topic of my recent CUP monograph 'Nonllinear Markov processes and kinetic equations'.

We show how to solve optimization problems in the presence of proportional transaction costs by determining a shadow price, which is a solution to the dual problem. Put differently, this is a fictitious frictionless market evolving within the bid-ask spread, that leads to the same optimization problem as in the original market with transaction costs. In addition, we also discuss how to obtain asymptotic expansions of arbitrary order for small transaction costs. This is joint work with Stefan Gerhold, Paolo Guasoni, and Walter Schachermayer.

We present theory and numerics of affine processes and several of their applications in finance. The theory is appealing due to methods from probability theory, analysis and geometry. Applications are diverse since affine processes combine analytical tractability with a high flexibility to model stylized facts like heavy tails or stochastic volatility.