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We present a simple adjustment to the single-factor credit capital model, which recognizes the diversification from a multi-factor model. We introduce the concept of a diversification factor at the portfolio level, and show that it can be expressed as a function of two parameters that broadly capture the size (sector) concentration and the average cross-sector correlation. The model further supports an intuitive capital allocation methodology through the definition of marginal diversification factors at the sector or obligor level. We estimate the diversification factor for a family of models, and show that it can be express in parametric form or tabulated for potential regulatory applications and risk management. As a risk management tool, it can be used to understand concentration risk, capital allocation and sensitivities, stress testing, as well as to compute "real-time" marginal risk.

The Gaussian factor copula model is the market standard model for multi-name credit derivatives. Its main drawback is that factor copula models exhibit correlation smiles when calibrating against market tranche quotes. To overcome the calibration deficiency, we introduce a multi-period factor copula model by chaining one-period factor copula models. The correlation coefficients in our model are allowed to be timedependent, and hence they are allowed to follow certain stochastic processes. Therefore, we can calibrate against market quotes more consistently. Usually, multi-period factor copula models require multi-dimensional integration, typically computed by Monte Carlo simulation, which makes calibration extremely time consuming. In our model, the portfolio loss of a completely homogeneous pool possesses the Markov property, thus we can compute the portfolio loss distribution analytically without multi-dimensional integration. Numerical results demonstrate the efficiency and flexibility of our model to match market quotes.