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In the top-down approach to multi-name credit modeling, calculation of singename sensitivities appears possible, at least in principle, within theso-called random thinning (RT) procedure which dissects the portfolio risk intoindividual contributions. We make an attempt to construct a practical RTframework that enables efficient calculation of single name sensitivities in atop-down framework, and can be extended to valuation and risk management ofbespoke tranches. Furthermore, we propose a dynamic extension of the RT methodthat enables modeling of both idiosyncratic and default-contingent individualspread dynamics within a Monte Carlo setting in a way that preserves theportfolio "top"-level dynamics. This results in a model that is not onlycalibrated to tranche and single name spreads, but can also be tuned toapproximately match given levels of spread volatilities and correlations ofnames in the portfolio.

Climbing Down from the Top: Single Name Dynamics in Credit Top Down Models