The purpose of this article is to study mortality-based securities, such as mortality bonds and swaps, and to price the proposed mortality securities. We focus on individual annuity data, although some of the modeling techniques could be applied to other lines of annuity or life insurance. Copyright The Journal of Risk and Insurance.
ABSTRACT. The values of life insurance and annuity liabilities move in opposite directions in response to a change in the underlying mortality. Natural hedging utilizes this to stabilize aggregate liability cash flows. We find empirical evidence that suggests that annuity writing insurers who use natural hedging also charge lower premiums than otherwise similar insurers. This indicates that insurers who are able to utilize natural hedging have a competitive advantage. In addition, we show how a mortality swap might be used to provide the benefits of natural hedging.
This paper provides an overview of ChatGPT, a natural language processing (NLP) system developed by Open AI. It discusses the features of ChatGPT, its benefits, and its challenges. The paper also provides an analysis of the potential applications of ChatGPT and its limitations. The paper concludes that ChatGPT is a powerful NLP system that can generate human-like conversations, but it has some challenges that must be addressed.
Normalized exponential tilting is an extension of classical theories, including the Capital Asset Pricing Model (CAPM) and the Black-Merton-Scholes model, to price risks with general-shaped distributions. The need for changing multivariate probability measures arises in pricing contingent claims on multiple underlying assets or liabilities. In this article, we apply it to valuation of mortality-based securities written on mortality indices of several countries. We show how to use multivariate exponential tilting to price the first pure mortality security, the Swiss Re bond. The same technique can be applied in other mortality securitization pricing. Copyright The Journal of Risk and Insurance, 2006.
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