Modeling dependency between operational risk losses and macroeconomic variables using Hidden Markov Models
Predicting when banks will suffer losses by tracking economic health
Banks lose money unpredictably—and those losses often spike when the economy weakens. Researchers built a statistical model that tracks hidden economic states and uses them to forecast operational losses, showing that macroeconomic conditions like unemployment and interest rates do meaningfully predict when these costly failures will occur.
Banks must set aside capital reserves for potential losses, and stress-testing requirements force them to model worst-case scenarios. A better prediction method could help regulators and banks estimate required reserves more accurately, avoiding either dangerously low buffers or wasteful overprovision. This affects lending capacity and ultimately how much credit flows to the real economy.