João Pires da Cruz, João Periquito Jarego, André Correia dos Santos
The recent crises in financial markets demonstrated that the lack of stationary economic properties in stochastic processes can be a limitation when it propagates into credit risk modeling. To incorporate uncertainty in stochastic metrics, we take advantage of a quantum mechanics framework to redefine the existing credit risk models in order to forecast a final risk distribution. In this work, we define the obligor as a wave function with intrinsically uncertain properties that will produce different risk measures with different probabilities. These probabilities can be derived in order to obtain a complete probability density function that incorporates the lack of stationarity of the variables. This can be achieved without the need to significantly adjust the information infrastructure that underpins the data and models.
