The paper on the pricing of catastrophe options just appeared in the Proceedings of the Actuarial and Financial Mathematics Conference.
In complete markets, pricing financial products is easy (at least from a theoretical point of view). In incomplete markets (e.g. when the underlying process has jumps with random size, such as an insurance loss process), the price is no longer unique. So on the one hand, it becomes difficult to provide a tractable price of insurance-linked derivatives. On the other hand, when facing catastrophic losses, using the pure premium as a price might not be relevant (e.g. for solvency issues). Both financial market and (re)insurance industry have proposed techniques to price identical hedging products that can be related (e.g. Esscher transform and more generally distorted risk measures in insurance, Gerber-Shiu transform in finance). In this paper, we focus on indifference utility techniques, assuming that stock prices have jumps,related to major catastrophic losses, and thus, partial hedging should then be possible.
La conférence cette année se tiendra les 5 et 6 février (site) a Bruxelles.
Exposé à Lyon dans le cadre du projet ANR AST& Risk.
In this talk, we consider optimal reinsurance from an insurer’s point of view. Given a (low) ruin probability target, the insurers wants to find the optimal risk transfer mechanism, i.e. either a proportional or a nonproportional reinsurance treaty. In the first case, a simple Monte Carlo algorithm can be designed, but in the nonproportional case, so far, no simple (and efficient) algorithm has been proposed
Value-at-Risk, despite being adopted as the standard risk measure in finance, suffers severe objections from a practical point of view, due to a lack of convexity, and since it does not reward diversification (which is an essential feature in portfolio optimization). Furthermore, it is also known as having poor behavior in risk estimation (which has been justified to impose the use of parametric models, but which induces then model errors). The aim of this paper is to chose in favor or against the use of VaR but to add some more information to this discussion, especially from the estimation point of view. Here we propose a simple method not only to estimate the optimal allocation based on a Value-at-Risk minimization constraint, but also to derive— empirical—confidence intervals based on the fact that the underlying distribution is unknown, and can be estimated based on past observations.