matter of a particular correlation model, but rather an issue of "irrational complacency". In the extremely benign time period from 2003 to 2006, proper hedging, proper risk management and stress test results were largely ignored. The prime example is AIG's London subsidiary, which had sold credit default swaps and collateralized debt obligations in an amount of close to $500 billion without conducting any major hedging. For an insightful paper on inadequate risk management leading up to the crisis, see “A personal view of the crisis – Confessions of a Risk Manager” (The Economist 2008). In particular, if any credit correlation model is fed with benign input data as low default intensities and low default correlation, the risk output figures will be benign, ‘garbage in garbage out’ in modeling terminology. Dynamic copulas A core enhancement of copula models are dynamic copulas, introduced by Albanese et al. (2005) and (2007). The "dynamic conditioning" approach models the evolution of multi-factor super-lattices, which correlate the return processes of each entity at each time step. Binomial dynamic copulas apply combinatorial methods to avoid Monte Carlo simulations. Richer dynamic Gaussian copulas apply Monte Carlo simulation and come at the cost of requiring powerful computer technology. Conditionally independent default (CID) correlation modeling In order to avoid specifying the default correlation between each entity pair in a portfolio a factorization is often applied. This leads to conditionally independent default (CID) modeling. The most widely applied CID model is the one-factor Gaussian copula (OFGC) model. It was the de facto market model for pricing CDOs before the 2007/2008 global financial crisis. The core equation of the OFGC model (10) where and are random drawings from and . As a result, the latent variable , sometimes interpreted as the asset value of i, see Turc, Very, Benhamou and Alvarez et al. (2005), is also n~(0,1). The common factor can be interpreted as the economic environment, possibly represented by the return of the S&P 500. is the idiosyncratic component, the ‘strength’ of entity i, possibly measured by entity i's stock price return. From equation (10) we see, that the correlation between entities i is modeled indirectly by conditioning the latent variable on the common factor . For example, for p =1, the latent variables of all entities , so the are identical in each simulation. For p = 0, all latent variable for all entities , hence the are independent. Importantly,