QuSAA – Quantum Algorithms for Strategic Asset Allocation

Asset allocation has been a focus of our department »Financial Mathematics« for many years. This refers to the allocation of assets to various asset classes such as bonds, equities, real estate, currencies and precious metals. Our mathematics has been supporting the portfolio management of R+V Lebensversicherung AG for years. In the BMBF project »QuSAA – Quantum Algorithms for Strategic Asset Allocation«, a team led by Dr. Pascal Halffmann and Dr. Ivica Turkalj, together with our project partners, is now also investigating possible new approaches using quantum computing (QC).

At least once a year, many companies and investors are faced with the question of how best to invest their existing capital in the coming year. The assessment of »best« encompasses several points in which the respective objectives must be reconciled. Particularly in view of several crises and economic uncertainties, it is currently more complex than ever to make these portfolio decisions. Our algorithms and software help with this.

Special Case: Asset Allocation of Insurance Companies

It is important for insurance companies to achieve the highest possible long-term return with a defined level of risk. However, special framework conditions apply to insurance companies, which must be taken into account in every calculation: Under the name »Solvency II«, the EU issued a directive in 2009 that has applied to all insurance companies since 2016. Solvency II places particular demands on companies' capital adequacy and is therefore another important target function in asset allocation and the work of our department »Financial Mathematics«. Together with R+V Lebensversicherung AG, Halffmann's team has already implemented its own approach to strategic asset allocation. This takes into account the solvency ratio under Solvency II on the one hand and incorporates many other relevant portfolio characteristics on the other.

In the project, the participants formulate an optimization problem by gradually simplifying the complexity of the target functions. The asset class data required for the optimization is based on estimated values from very computationally complex simulations. This is where quantum computers come into play, as the project »QuSAA« is investigating the extent to which computing on quantum computers can help to better manage the complexity of the problem and deliver more robust results. But the »how« still needs to be researched.