Monte carlo within simulated annealing for integral constrained optimizations

For years, Value-at-Risk and Expected Shortfall have been well established measures of market risk and the Basel Committee on Banking Supervision recommends their use when controlling risk. But their computations might be intractable if we do not rely on simplifying assumptions, in particular on distributions of returns. One of the difficulties is linked to the need for Integral Constrained Optimizations. In this article, two new stochastic optimization-based Simulated Annealing algorithms are proposed for addressing problems associated with the use of statistical methods that rely on extremizing a non-necessarily differentiable criterion function, therefore facing the problem of the computation of a non-analytically reducible integral constraint. We first provide an illustrative example when maximizing an integral constrained likelihood for the stress-strength reliability that confirms the effectiveness of the algorithms. Our results indicate no clear difference in convergence, but we favor the use of the problem approximation strategy styled algorithm as it is less expensive in terms of computing time. Second, we run a classical financial problem such as portfolio optimization, showing the potential of our proposed methods in financial applications.

Automated summary

This article introduces two new stochastic optimization-based simulated annealing algorithms for addressing problems associated with statistical methods. These methods are effective in handling integral constrained optimization problems and show potential in financial applications.