For purposes of Value-at-Risk estimation, we consider three multivariate
families of heavy-tailed distributions, which can be seen as multidimensional
versions of Paretian stable and Student's t distributions allowing different
marginals to have different tail thickness. After a discussion of relevant
estimation and simulation issues, we conduct a backtesting study on a set of
portfolios containing derivative instruments, using historical US stock price
data.
We prove existence of weak solutions (in the probabilistic sense) for a
general class of stochastic semilinear wave equations on bounded domains of
$R^d$ driven by a possibly discontinuous square integrable martingale.
In the semigroup approach to stochastic evolution equations, the fundamental
issue of uniqueness of mild solutions is often "reduced" to the much easier
problem of proving uniqueness for strong solutions. This reduction is usually
carried out in a formal way, without really justifying why and how one can do
that. We provide sufficient conditions for uniqueness of mild solutions to a
broad class of semilinear stochastic evolution equations with coefficients
satisfying a monotonicity assumption.
We study the asymptotic behavior of solutions to stochastic evolution
equations with monotone drift and multiplicative Poisson noise in the
variational setting, thus covering a large class of (fully) nonlinear partial
differential equations perturbed by jump noise. In particular, we provide
sufficient conditions for the existence, ergodicity, and uniqueness of
invariant measures. Furthermore, under mild additional assumptions, we prove
that the Kolmogorov equation associated to the stochastic equation with
additive noise is solvable in $L_1$ spaces with respect to an invariant
measure.
We prove global well-posedness in the strong sense for stochastic generalized
porous media equations driven by square integrable martingales with stationary
independent increments.