Yang Shen, Michael Sherris, Yawei Wang, Jonathan Ziveyi
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引用次数: 0
Abstract
This paper proposes a dynamic longevity risk hedging strategy for smooth survival benefit profiles of group self-annuity (GSA) schemes in the presence of population basis risk. The fund manager of GSA acts on behalf of fund participants in selecting the optimal hedge. The hedging framework is formulated as a mean-variance optimization problem, which serves as a theoretical framework for selecting the optimal hedging strategy. The hedging mechanism involves trading standardized longevity-linked securities dynamically. A semi-analytic solution to the optimal hedge ratio is derived, which enhances the numerical implementation of the strategy. Furthermore, a risk decomposition method is developed, enabling hedging of various sources of risks, such as longevity and investment risks. Numerical illustrations highlight that the hedging strategy effectively mitigates variability in survival benefits. Meanwhile, a holistic risk management framework utilizing the longevity risk hedging strategy and a target volatility investment strategy increases the fund's return per unit of risk.
期刊介绍:
The Journal of Risk and Insurance (JRI) is the premier outlet for theoretical and empirical research on the topics of insurance economics and risk management. Research in the JRI informs practice, policy-making, and regulation in insurance markets as well as corporate and household risk management. JRI is the flagship journal for the American Risk and Insurance Association, and is currently indexed by the American Economic Association’s Economic Literature Index, RePEc, the Social Sciences Citation Index, and others. Issues of the Journal of Risk and Insurance, from volume one to volume 82 (2015), are available online through JSTOR . Recent issues of JRI are available through Wiley Online Library. In addition to the research areas of traditional strength for the JRI, the editorial team highlights below specific areas for special focus in the near term, due to their current relevance for the field.