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QuSAA

Quantum Algorithms for Strategic Asset Allocation

The project is funded by the Federal Ministry of Education and Research (BMBF):

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Project Description

The aim of the project is to support strategic asset allocation and, in particular, to incorporate the capital requirements according to Solvency II (solvency ratio) into this investment decision. For this purpose, it will be investigated to what extent the use of quantum computers can contribute to a better control of the complexity of the problem and to deliver more stable results. In doing so, the optimization problem is formulated step by step at different levels of complexity. The solvency ratio and the stochastic optimization will be investigated in the further course and the model formulated in the beginning will be extended. Thereby it shall be examined, which hardware requirements can be derived to estimate a temporal availability. Likewise, the foundations for a cost/benefit analysis are to be laid.

Partner

Mohammad Assadsolimani

"The application of Quantum Computing in finance is a rather new. QuSAA is a marvelous chance to apply it in the strategic asset allocation accompanied by Jos QUANTUM and Fraunhofer ITWM."

Dr Thomas Decker

Thomas Decker

"In QuSAA we use quantum algorithms to improve models for asset allocation. Our goal is to replace computationally expensive processes in classical models with better performing quantum algorithms."

Jörg Wenzel

"Together with JoS QUANTUM and R+V Versicherung, we make investment decisions safer and risks easier to assess. In this project, we develop cutting edge algorithms for optimization of asset allocation processes."

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JoS QUANTUM GmbH (www.jos-quantum.de) develops software and algorithms for the financial, insurance and energy industry and connects special hardware, such as various quantum computers and quantum simulators and special purpose chips in one platform. Focusing on the toughest problems within the field, JSQ provides easy integratable cutting edge computing algorithms for risk management, portfolio optimization, portfolio allocation, stress testing and network analysis. An API to the platform allows computationally intensive steps to be solved efficiently and integrated as microservices into any infrastructure. Furthermore, JSQ provides unique interconnected expertise in finance, insurance, energy, software development, algorithm and quantum algorithm design.

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R+V Versicherung AG (Raiffeisen- und Volksbanken Versicherung) is one of Germany’s largest insurance companies and the parent company of the R+V Group, headquartered in Wiesbaden, Hesse. It holds a majority stake, either directly or indirectly, in the primary insurance companies of the R+V Group and is part of the cooperative FinanzGruppe of the Bundesverband der Deutschen Volksbanken und Raiffeisenbanken e. V. (BVR). (BVR). It is also the central reinsurer of R+V’s primary insurance companies. It also operates independently in the international reinsurance market.

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Fraunhofer ITWM is the world’s largest institute for industrial mathematics. The department of Financial Mathematics has its methodological guidelines in financial mathematics and data science. Data science involves the use of methods from machine learning while Financial Mathematics includes stochastic modeling, simulation and optimization, as well as statistical methods and time series analysis. All business areas of the department (insurance, energy, and anomaly detection) investigate the use of quantum computing technology. The department can rely on a large experience in development of software prototypes, which are often the result of industrial projects.

News

Bildschirmfoto vom 2021-09-15 17-03-34

07.07.2021
Michael Meister, Parliamentary State Secretary at the BMBF, visits the new QuSAA funding project and talks to the project leaders about quantum computing, algorithms for portfolio optimization and possible added value for a more stable financial system.

Research

Multicriteria asset allocation in practice
19.03.2021

An improved hyperboxing algorithm for calculating a Pareto front representation
31.03.2020

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