Optimal credit allocation under regime uncertainty with sensitivity analysis

We consider the problem of credit allocation in a regime-switching model. The global evolution of the credit market is driven by a benchmark, the drift of which is given by a two-state continuous-time hidden Markov chain. We apply filtering techniques to obtain the diffusion of the credit assets under partial observation and show that they have a specific excess return with respect to the benchmark. The investor performs a simple mean–variance allocation on credit assets. However, returns and variance matrix have to be computed by a numerical method such as Monte Carlo, because of the dynamics of the system and the non-linearity of the asset prices. We use the theory of Dirichlet forms to deal with the uncertainty on the excess returns. This approach provides an estimation of the bias and the variance of the optimal allocation, and return. We propose an application in the case of a sectorial allocation with Credit Default Swaps (CDS), fully calibrated with observable data or direct input given by the portfolio manager.