Institutions with long-dated liabilities — pension insurers and funds, life and non-life insurers, banks, state financing and guarantee institutions, foundations and companies with large balance sheets — cannot manage risk by looking at their investments alone. What matters is the joint behaviour of assets and liabilities over time: solvency, funding ratio, liquidity, contribution and benefit levels, and the price at which risk is taken onto the balance sheet in the first place.
We build the quantitative models that answer these questions. Our method is multistage stochastic optimization: a scenario model of the economy and markets — interest rates, inflation, equity and credit returns, real assets, currencies — is combined with a model of the institution's own liabilities and cash flows, and the investment and hedging decisions are then optimized across time and across scenarios, subject to the regulatory and internal constraints that actually bind.
The difference to conventional approaches is practical, not academic. A traditional ALM study or a mean-variance optimization describes risk at a single point in time and produces a static allocation. Stochastic optimization produces a strategy: what to hold now, and how to react as funding levels, rates and markets move. It handles path dependency, solvency constraints, illiquid asset classes, contribution and indexation rules, and asymmetric objectives such as shortfall or ruin probability — all of which are precisely the features that determine outcomes in practice, and all of which single-period models are forced to ignore.
Internal models and the true adequacy of assets. We build internal models for the institution's own decision-making, and the question they answer is the one that ultimately matters: will the assets actually be sufficient to meet the liabilities as they fall due?
Regulatory solvency frameworks answer a narrower question — whether capital covers a one-year shock under prescribed assumptions. That is a necessary constraint, and it is well served by existing tools and consultants. It is not the same as knowing whether the money will suffice. Liabilities that run for decades are met from realized returns, contributions and cash flows over the whole horizon, and their adequacy depends on the paths markets take, not on a single stress figure. A balance sheet can be comfortably solvent on the regulatory measure and still be on a trajectory where the assets do not last — or be capital-constrained while the underlying position is sound.
Our internal models make that trajectory explicit: the full distribution of future funding and cash flow outcomes, the probability and timing of shortfall, what drives it, and which decisions today measurably improve it. Very few institutions have this view, and it is what changes decisions — the level of risk that is genuinely affordable, the contributions or pricing needed, and the hedges that are worth their cost. Regulatory and accounting constraints are modelled alongside, because they bind in practice, but they are constraints in the model rather than its objective.
Portfolio optimization. Determining the strategic allocation and the dynamic decision rules around it — how much interest rate and inflation risk to carry against the liabilities, how much illiquidity is affordable given cash flow needs, how much equity risk the solvency position can support at each funding level, and how the answer changes over the planning horizon.
Pricing. The same machinery prices the liabilities themselves. Guarantees, credit and export guarantee portfolios, insurance liabilities, long-term contracts and structured arrangements are valued consistently with the balance sheet risk and capital they consume, so that pricing, risk limits and solvency management rest on the same model rather than on three unconnected ones.
