A Portfolio Optimization Approach Using Combinatorics With a Genetic Algorithm for Developing a Reinsurance Model

Porth, Lysa; Pai, Jeffrey

doi:10.1111/jori.12037

Cited by 15 publications

(3 citation statements)

References 44 publications

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“…The goal here is to distribute an investor's capital on assets such that a given objective, such as maximizing the return or minimizing the risk, is optimized. Portfolio optimization is an actively researched question in scientific domains such as combinatorial optimization [13], operations research [14], data science [15], and the application of quantum computing [16]. Not only the academic community is focusing on this topic, but it is also a core business of banks and financial advisers and the combination of portfolio optimization with quantum techniques is addressed by numerous companies and consultants [17,18].…”

Section: Introductionmentioning

confidence: 99%

Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing

Lang

Zielinski²,

Feld

2022

Applied Sciences

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In this work, we introduce a new workflow to solve portfolio optimization problems on annealing platforms. We combine a classical preprocessing step with a modified unconstrained binary optimization (QUBO) model and evaluate it using simulated annealing (classical computer), digital annealing (Fujitsu’s Digital Annealing Unit), and quantum annealing (D-Wave Advantage). Starting from Markowitz’s theory on portfolio optimization, our classical preprocessing step finds the most promising assets within a set of possible assets to choose from. We then modify existing QUBO models for portfolio optimization, such that there are no limitations on the number of assets that can be invested in. Furthermore, our QUBO model enables an investor to also place an arbitrary amount of money into each asset. We apply this modified QUBO to the set of promising asset candidates we generated previously via classical preprocessing. A solution to our QUBO model contains information about what percentage of the whole available capital should be invested into which asset. For the evaluation, we have used publicly available real-world data sets of stocks of the New York Stock Exchange as well as common ETFs. Finally, we have compared the respective annealing results with randomly generated portfolios by using the return, variance, and diversification of the created portfolios as measures. The results show that our QUBO formulation is capable of creating well-diversified portfolios that respect certain criteria given by an investor, such as maximizing return, minimizing risk, or sticking to a certain budget.

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Section: Introductionmentioning

confidence: 99%

Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing

Lang

Zielinski²,

Feld

2022

Applied Sciences

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“…A major challenge facing the agricultural sector is that weather risk is systematic and undiversifiable in the sense that it is outside the control of human management, and at times weather risk can be widespread and spatially correlated, impacting many farms within a region (Woodard et al, 2012;Porth et al, 2015). Therefore, weather risk will not be eliminated by pooling, and must be managed through various risk transfer techniques.…”

Section: Introductionmentioning

confidence: 99%

“…To alleviate an insurer's exposure to large potential losses, private reinsurance is often purchased in addition to pooling to help diversify its portfolio of crop risks (Miranda and Glauber, 1997). For example, Porth et al (2015) show that risk transfer (private reinsurance) is necessary in order to help ensure the solvency of crop insurance companies in Canada. Further, a study from Qatar Re shows that almost 80% of the global downside risk for agricultural insurers are reinsured (Schneider and Roth, 2013).…”

Section: Introductionmentioning

confidence: 99%

Spatial Dependence and Aggregation in Weather Risk Hedging: A Lévy Subordinated Hierarchical Archimedean Copulas (Lshac) Approach

Zhu¹,

Tan²,

Porth³

et al. 2018

ASTIN Bull.

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Adverse weather-related risk is a main source of crop production loss and a big concern for agricultural insurers and reinsurers. In response, weather risk hedging may be valuable, however, due to basis risk it has been largely unsuccessful to date. This research proposes the Lévy subordinated hierarchical Archimedean copula model in modelling the spatial dependence of weather risk to reduce basis risk. The analysis shows that the Lévy subordinated hierarchical Archimedean copula model can improve the hedging performance through more accurate modelling of the dependence structure of weather risks and is more efficient in hedging extreme downside weather risk, compared to the benchmark copula models. Further, the results reveal that more effective hedging may be achieved as the spatial aggregation level increases. This research demonstrates that hedging weather risk is an important risk management method, and the approach outlined in this paper may be useful to insurers and reinsurers in the case of agriculture, as well as for other related risks in the property and casualty sector.

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Evolutionary Machine Learning in Finance

O’Neill,

Brabazon

2023

Genetic and Evolutionary Computation

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A Portfolio Optimization Approach Using Combinatorics With a Genetic Algorithm for Developing a Reinsurance Model

Cited by 15 publications

References 44 publications

Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing

Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing

Spatial Dependence and Aggregation in Weather Risk Hedging: A Lévy Subordinated Hierarchical Archimedean Copulas (Lshac) Approach

Evolutionary Machine Learning in Finance

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