A Simheuristic Algorithm for Reliable Asset and Liability Management Under Uncertainty Scenarios

Combining a Matheuristic with Simulation for Risk Management of Stochastic Assets and Liabilities

“…Probably, the most interesting comparison in this table is between columns BR and Stoch. As one can see, the proposed matheuristic-simulation algorithm is usually able to outperform the previous simulation-optimisation approach proposed in Bayliss et al (2020). This is mainly due to the fact that the methodology proposed in this paper does not require to assume a one-to-one mapping between assets and liabilities, thus allowing for an increasing number of mapping combinations.…”

“…The proposed heuristic has been implemented as a Python application running on a CPU with 3.60 GHz and 16 GB of RAM. Instances from Bayliss et al (2020) have been used to test the new approach, plus two new instances that could not be solved with the methodology presented in the former paper. Table 1 provides the details on the number of assets and liabilities for each instance, discount rate, and value modifier (if any was employed).…”

“…500 iterations are executed for each asset-liability assignment generated in the Monte-Carlo simulation. Table 2 provides the experimental results, compared with the results obtained in Bayliss et al (2020). The first column contains the instance number (same as in Table 1).…”

“…The goal is to find the most efficient (minimum cost) combination of assets that meets certain requirements: they must generate sufficient income to cover the obligations of the insurer with a high probability. In a recent work, Bayliss et al (2020) considered a simplified ALM problem, based on the net present value (NPV) concept, in which only one-to-one asset-liability assignment were allowed. Notice that, since we are comparing monetary values of assets that belong to different time periods, it makes sense to consider the NPV associated with each asset in order to make a fairer comparison of assets.…”