Berlin

In this article we propose a novel approach to reduce the computational

complexity of the dual method for pricing American options.

We consider a sequence of martingales that converges to a given

target martingale and decompose the original dual representation into a sum of

representations that correspond to different levels of approximation to the

target martingale. By next replacing in each representation true conditional expectations with their

Monte Carlo estimates, we arrive at what one may call a multilevel dual Monte

Carlo algorithm. The analysis of this algorithm reveals that the computational

complexity of getting the corresponding target upper bound, due to the target martingale,

can be significantly reduced. In particular, it turns out that using our new

approach, we may construct a multilevel version of the well-known nested Monte

Carlo algorithm of Andersen and Broadie (2004) that is, regarding complexity, virtually

equivalent to a non-nested algorithm. The performance of this multilevel

algorithm is illustrated by a numerical example. (joint work with Denis Belomestny)

In this paper we deal with the utility maximization problem with a

preference functional of expected utility type. We derive a new approach

in which we reduce the utility maximization problem with general utility

to the study of a fully-coupled Forward-Backward Stochastic Differential

Equation (FBSDE).

The talk is based on joint work with Ying Hu, Peter Imkeller, Anthony

Reveillac and Jianing Zhang.

The phase transition is a phenomenon that appears in natural sciences in various contexts. In the random graph theory, the phase transition refers to a dramatic change in the number of vertices in the largest components by addition of a few edges around a critical value, which was first discussed on the standard random graphs in the seminal paper by Erdos and Renyi. Since then, the phase transition has been a central theme of the random graph theory. In this talk we discuss the phase transition in random graphs with a given degree sequence and random graph processes with degree constraints.

When liquidating large portfolios of securities one faces a trade off between adverse market impact of sell orders and the impatience to generate proceeds. We present a Black-Scholes model with an impact factor describing the market's distress arising from previous transactions and show how to solve the ensuing optimization problem via classical calculus of variations. (Joint work with Dirk Becherer, Humboldt Universität zu

Berlin)

We discuss recent advances in the probabilistic analysis of financial risk and uncertainty, including risk measures and their dynamics, robust portfolio choice, and some asymptotic results involving large deviations

In Dept of Statistics

Recently random planar structures, such as planar graphs and outerplanar graphs, have received much attention. Typical questions one would ask about them are the following: how many of them are there, can we sample a random instance uniformly at random, and what properties does a random planar structure have ? To answer these questions we decompose the planar structures along their connectivity. For the asymptotic enumeration we interpret the decomposition in terms of generating funtions and derive the asymptotic number, using singularity analysis. For the exact enumeration and the uniform generation we use the so-called recursive method: We derive recursive counting formulas along the decomposition, which yields a deterministic polynomial time algorithm to sample a planar structure that is uniformly distributed. In this talk we show how to apply these methods to several labeled planar structures, e.g., planar graphs, cubic planar graphs, and outerplanar graphs.

In Corpus