  In the top-down approach to multi-name credit modeling, calculation of singe
name sensitivities appears possible, at least in principle, within the
so-called random thinning (RT) procedure which dissects the portfolio risk into
individual contributions. We make an attempt to construct a practical RT
framework that enables efficient calculation of single name sensitivities in a
top-down framework, and can be extended to valuation and risk management of
bespoke tranches. Furthermore, we propose a dynamic extension of the RT method
that enables modeling of both idiosyncratic and default-contingent individual
spread dynamics within a Monte Carlo setting in a way that preserves the
portfolio "top"-level dynamics. This results in a model that is not only
calibrated to tranche and single name spreads, but can also be tuned to
approximately match given levels of spread volatilities and correlations of
names in the portfolio.
