TITLE:
Scenario Generation for Asset and Liability Management Models Applied to a Saudi Arabian Pension Fund
AUTHORS:
Maram Alwohaibi, Diana Roman, Alina Peluso
KEYWORDS:
Asset and Liability Management, Liability Driven Investment, Risk Man-agement, Funding Ratio, Population Modelling, Historical Copula
JOURNAL NAME:
Journal of Financial Risk Management,
Vol.11 No.2,
May
11,
2022
ABSTRACT: In Asset and Liability Management (ALM) models, there are parameters
whose values are not known with certainty at decision time, such as future
asset returns, liability and contribution values. Simulation models generate
possible “scenarios” for these parameters, which are used as inputs in the
optimisation models and help thus in making decisions. These decisions can be
evaluated in the sample, on the same scenarios that were
used for making the decision, and out-of-sample, on a
different, usually much larger, scenario set. With asset return simulation, the
major difficulty lies in the multivariate nature of the data. We propose to
capture this via the historical copula, making thus no distributional
assumptions. We suggest the use of univariate sample generation which allows
for different asset returns to be modelled by different distributions. The
liabilities and contributions values have as a main source of uncertainty the
population numbers; we propose to model this by adapting a model used in
biology (BIDE). We use the resulting scenario generator in four different ALM
optimisation models, using a dataset from the largest Saudi Arabian pension
fund and the Saudi Arabian market index.