Why should risk management systems account for parameter
uncertainty? In order to answer this question, this
paper lets an investor in a credit portfolio face
non-diversifiable estimation-driven uncertainty about
two parameters: probability of default and asset-return
correlation. Bayesian inference reveals that - for
realistic assumptions about the portfolio's credit
quality and the data underlying parameter estimates -
this uncertainty substantially increases the tail risk
perceived by the investor. Since incorporating parameter
uncertainty in a measure of tail risk is computationally
demanding, the paper also derives and analyzes a
closed-form approximation to such a measure. pdf 2009
