Optimal insurance portfolios risk-adjusted performance through dynamic stochastic programming

• Published in: Computational management science 2018-10, Vol.15 (3-4), p.599-632
• Main Authors: Consigli, Giorgio, Moriggia, Vittorio, Vitali, Sebastiano, Mercuri, Lorenzo
• Format: Article
• Language: English
• Subjects: Asset liability management; Business and Management; Correlation analysis; Insurance; Mathematical analysis; Matrix methods; Operations Research/Decision Theory; Optimization; Original Paper; Personal computers; Regulators; Risk allocation; Risk management; Stochastic programming
• ISSN: 1619-697X; 1619-6988
• DOI: 10.1007/s10287-018-0328-7

The practical adoption of the Solvency II regulatory framework in 2016, together with increasing property and casualty (PC) claims in recent years and an overall reduction of treasury yields across more developed financial markets have profoundly affected traditional risk management approaches by insurance institutions. The adoption of firm-wide risk capital methodologies to monitor the companies’ overall risk exposure has further consolidated the introduction of risk-adjusted performance measures to guide the management medium and long-term strategies. Relying on a dynamic stochastic programming formulation of a 10 year asset-liability management (ALM) problem of a PC company, we analyse in this article the implications on capital allocation and risk-return trade-offs of an optimization problem developed for a global insurance company based on a pair of risk-adjusted return functions. The analysis is relevant for any institutional investor seeking a high risk-adjusted performance as for regulators in their structuring of stress-tests and effective regulatory frameworks. The introduction of the concept of risk capital, or economic capital, in the definition of medium and long term insurance strategies poses a set of modeling and methodological issues tackled in this article. Of particular interest is the study of optimal ALM policies under different assets’ correlation assumptions. From a computational viewpoint it turns out that, depending on the assumed correlation matrix, the stochastic program is linear or of second order conic type. A case study from a real-world company development is presented to highlight the effectiveness of applied stochastic programming in capturing complex risk and return dynamics arising in modern corporate finance and lead to an efficient long-term financial allocation process.