University of Bonn

When managing risk, frequently only imperfect hedging instruments are at hand.

We show how to optimally cross-hedge risk when the spread between the hedging

instrument and the risk is stationary. At the short end, the optimal hedge ratio

is close to the cross-correlation of the log returns, whereas at the long end, it is

optimal to fully hedge the position. For linear risk positions we derive explicit

formulas for the hedge error, and for non-linear positions we show how to obtain

numerically effcient estimates. Finally, we demonstrate that even in cases with no

clear-cut decision concerning the stationarity of the spread it is better to allow for

mean reversion of the spread rather than to neglect it.

The talk is based on joint work with Georgi Dimitroff, Gregor Heyne and Christian Pigorsch.

Partial differential equations with a nonlinear pointwise constraint defined through a manifold occur in a variety of applications: The magnetization of a ferromagnet can be described by a unit length vector field and the orientation of the rod-like molecules that constitute a liquid crystal is often modeled by a vector field that attains its values in the real projective plane thus respecting the head-to-tail symmetry of the molecules. Other applications arise in geometric

modeling, quantum mechanics, and general relativity. Simple examples reveal that it is impossible to satisfy pointwise constraints exactly by lowest order finite elements. For two model problems we discuss the practical realization of the constraint, the efficient solution of the resulting nonlinear systems of equations, and weak accumulation of approximations at exact solutions.

Sequential Monte Carlo Samplers are a class of stochastic algorithms for

Monte Carlo integral estimation w.r.t. probability distributions, which combine

elements of Markov chain Monte Carlo methods and importance sampling/resampling

schemes. We develop a stability analysis by functional inequalities for a

nonlinear flow of probability measures describing the limit behaviour of the

methods as the number of particles tends to infinity. Stability results are

derived both under global and local assumptions on the generator of the

underlying Metropolis dynamics. This allows us to prove that the combined

methods sometimes have good asymptotic stability properties in multimodal setups

where traditional MCMC methods mix extremely slowly. For example, this holds for

the mean field Ising model at all temperatures.

http://sag.maths.ox.ac.uk/samath/seminars/Sturm.pdf