Research of the Actuarial Science & Mathematical Finance (ASMF) group is mainly directed at insurance companies (for life, non-life, pension and social insurance) and pension funds, but also considers other financial intermediaries. Research is performed on the mathematical modelling, estimation, evaluation and control of the risks related to such financial institutions, under complete and incomplete information and for complete and incomplete markets. This typically involves the development of new mathematical and statistical methods and applications thereof in optimal control and asset pricing.
Within the ASMF research programme, there is an emphasis on 3 areas:
Asset and liability management for insurance contracts and pension obligations can often be formulated in terms of optimal investment and consumption problems. Typical for such actuarial risk management problems are the relatively long time horizons, necessitating investment in less liquid assets and making the mitigation of interest rate risk more difficult. For pension funds, investment goals are often stated in real instead of nominal terms which complicates the dynamic optimisation problem. There is also the added challenge of longevity risk, since life expectancy continues to rise faster than was predicted earlier in most Western countries. Stochastic models for liquidity, inflation, and future survival rates, and their consequences for the valuation and risk management of annuities and pension contracts therefore constitute an important area of investigation.
Methods for risk measurement and the determination of solvency requirements have come under intensified scrutiny in the wake of the financial crisis of 2008. The new European regulatory framework Solvency II for insurers and the Dutch FTK regulation for pension funds have therefore led to many new research questions.
Researchers in this programme contribute fundamentally to mathematical models for market-consistent pricing in incomplete markets and to the further development of actuarial risk theory. This includes the development of robust methods for risk measurement and management that explicitly take model risk (ambiguity) into account. It also includes the development of methods to evaluate and mitigate systemic risk in the broader financial sector. From an applied perspective, researchers in this programme actively participate as scientific advisors in policy discussions on the new architecture of financial regulation and supervision.
Present-day challenges in non-life insurance contracts include the detection of insurance fraud, premium rating and claims reserving and the estimation of unreported claims. In this research programme techniques have been developed which tackle such issues by the use of Generalised Linear Mixed Models. These methods can also be used for survival modelling, graduation, multiple-state insurance models and risk classification and as such allow the unification of several distinct actuarial subfields.
Members of the research programme participate in the Tinbergen Institute (the graduate school and research institute of the University of Amsterdam, the Erasmus University Rotterdam and the VU University Amsterdam) and the Amsterdam Centre of Excellence in Risk and Macro Finance. They also participate in scientific networks outside the University of Amsterdam such as AMaMeF, EDEEM, Eurandom, and Netspar.
In the last decade, part of the research has been carried out with the support of Netspar research grants (Valuation and Risk Management for Insurance Companies and Pension Funds, Reconciling Short Term Risks and Long term Goals in Retirement Provisions and Risk Management in Funded Pension Systems), an NWO VENI grant (Non-life: A Life Insurance Approach), and an NWO VIDI grant (Econometrics of Contagion in Insurance and Finance). The Chair of Risk and Insurance is sponsored by the Dutch Association of Insurers. A number of PhD positions have been partially funded by, among others, APG, Van Ameyde and VIVAT.
The ASMF research programme organises several seminars and events per year.
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