TY - JOUR
T1 - Optimal insurance portfolios risk-adjusted performance through dynamic stochastic programming
AU - Consigli, Giorgio
AU - Moriggia, Vittorio
AU - Vitali, Sebastiano
AU - Mercuri, Lorenzo
N1 - Funding Information:
We acknowledge the cooperation of Dr. Giacomo Landoni during part of this development and the long-term cooperation with Dr. Massimo di Tria, currently Chief Investment Officer at Cattolica Assicurazioni in Italy and previously deputy Head of ALM at Allianz Investment Management. Dr. Lorenzo Mercuri was funded during a 1 year PostDoc position at University of Bergamo by the grant Dote Ricercatori - EU - Regione Lombardia Project 11630 2014 (POR - FSE2007-b). The research was partially supported by MIUR-ex60% 2017 and MIUR-ex60% 2018 sci.resp. Vittorio Moriggia, and by MIUR-ex60% 2016–2017 sci.resp. Giorgio Consigli. The research of Sebastiano Vitali was supported by Czech Science Foundation Project GACˇ R No. 18-01781Y. This work is in memory of professor Maarten van der Vlerk..
Funding Information:
We acknowledge the cooperation of Dr. Giacomo Landoni during part of this development and the long-term cooperation with Dr. Massimo di Tria, currently Chief Investment Officer at Cattolica Assicurazioni in Italy and previously deputy Head of ALM at Allianz Investment Management. Dr. Lorenzo Mercuri was funded during a 1?year PostDoc position at University of Bergamo by the grant Dote Ricercatori - EU - Regione Lombardia Project 11630 2014 (POR - FSE2007-b). The research was partially supported by MIUR-ex60% 2017 and MIUR-ex60% 2018 sci.resp. Vittorio Moriggia, and by MIUR-ex60% 2016?2017 sci.resp. Giorgio Consigli. The research of Sebastiano Vitali was supported by Czech Science Foundation Project GA?R No. 18-01781Y. This work is in memory of professor Maarten van der Vlerk.
Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
KW - Dynamic stochastic programming
KW - Property and casualty liabilities
KW - Return on risk-adjusted capital
KW - Risk capital allocation
KW - Surplus investment return
UR - http://www.scopus.com/inward/record.url?scp=85049058656&partnerID=8YFLogxK
U2 - 10.1007/s10287-018-0328-7
DO - 10.1007/s10287-018-0328-7
M3 - Article
AN - SCOPUS:85049058656
SN - 1619-697X
VL - 15
SP - 599
EP - 632
JO - Computational Management Science
JF - Computational Management Science
IS - 3-4
ER -