Consider an investor trading dynamically to maximize expected utility from
terminal wealth. Our aim is to study the dependence between her risk aversion
and the distribution of the optimal terminal payoff.
Economic intuition suggests that high risk aversion leads to a rather
concentrated distribution, whereas lower risk aversion results in a higher
average payoff at the expense of a more widespread distribution.
We discuss the possibility of obtaining model-free bounds on volatility
derivatives, given present market data in the form of a calibrated local
volatility model. A counter-example to a wide-spread conjecture is given.
The duality theory of the Monge--Kantorovich transport problem is analyzed in
a general setting. The spaces $X, Y$ are assumed to be polish and equipped with
Borel probability measures $\mu$ and $\nu$. The transport cost function
$c:X\times Y \to [0,\infty]$ is assumed to be Borel. Our main result states
that in this setting there is no duality gap, provided the optimal transport
problem is formulated in a suitably relaxed way.
The paper is accompanying "A general Duality Theorem for the
Monge-Kantorovich Transport Problem". We explain the methods used in this
article in an elementary setting and present two examples complementing the
results obtained therein.
It is well known and not difficult to prove that if $C$ of integers has
positive upper Banach density, the set of differences $C-C$ is syndetic, i.e.
the length of gaps is uniformly bounded. More surprisingly, Renling Jin showed
that whenever $A$ and $B$ have positive upper Banach density, then $A-B$ is
piecewise syndetic.