Special Session 118: Recent advances in mathematical finance

Dynamic portfolio risk budgeting through reinforcement learning

Giorgio Consigli
Khalifa University of Science and Technology
United Arab Emirates
Co-Author(s):    Sanabel Bisharat, Alvaro Gomez
Abstract:
In an early paper we have studied the correspondence between second order interval stochastic dominance (ISD-2) and interval conditional value-at-risk (ICVaR), a tail risk measure carrying specific properties and generalizing the popular conditional value-at-risk. Relying on the ICVaR, in this paper we present a reinforcement learning approach to solve a trade-off problem based on one side on a risk parity paradigm and on the other on an ICVaR function enforcing second order dominance with respect to a benchmark strategy. The bi-criteria objective helps clarifying the risk-budgeting implications induced by a progressive switch from risk parity towards SD against the benchmark for portfolio construction in a dynamic model. We consider a 1-year investment problem with an asset universe, or decision space of the problem, based on exchange traded funds (ETF) and market benchmarks spanning different risk classes. An extended in- and out-of-sample validation is performed on market data with a discussion on the computational properties of the problem.