Credit Risk Management Practice Exam 2026 – Complete All-in-One Guide to Master Your Exam!

The exponential distribution is commonly used to model the time until the next default event in credit risk management due to its memoryless property. This means that the likelihood of a default occurring in the next time interval is independent of how much time has already passed without a default. This characteristic aligns well with events that occur continuously and independently, such as defaults in a portfolio of loans or credit instruments.

In the context of credit risk, the focus is often on assessing the waiting time until a borrower defaults on their obligation. The exponential distribution provides a mathematically tractable way to express the probability of defaults over time, making it a preferred choice for this kind of modeling. The simplicity of the exponential distribution also allows for easier calculations of expected time until default and related risk metrics.

The other distributions mentioned, such as normal and uniform, do not possess the same characteristics that are appropriate for modeling such time-to-event data. The normal distribution is typically used for variables that exhibit symmetry, while the uniform distribution assumes a constant probability over a defined range, neither of which applies to the random nature of default events. The geometric distribution, on the other hand, relates more to the number of trials until the first success and is less relevant in this context. Thus, the exponential