Deep impulse control: application to interest rate intervention

Abstract

We propose a deep learning framework for impulse control problems involving multivariate stochastic processes, which can be controllable or uncontrollable. We use this framework to estimate central bank interventions on the (controllable) interest rate to stabilize the (uncontrollable) inflation rate, where the two rates are correlated and cointegrated. This method is useful for small banks or insurance companies with high exposure to Treasury securities to predict and stress-test their potential losses from central bank interventions. We also study the mathematical properties of the proposed framework.