Estimating the Volatility of Non-Life Premium Risk Under Solvency II: Discussion of Danish Fire Insurance Data

We studied the volatility assumption of non-life premium risk under the Solvency II Standard Formula and developed an empirical model on real data, the Danish fire insurance data. Our empirical model accomplishes two things. Primarily, compared to the present literature, this paper innovates the fitting of Danish fire insurance data using a composite model with a random threshold. Secondly we prove, by fitting the Danish fire insurance data, that for large insurance companies the volatility of the standard formula is higher than the volatility estimated with internal models such as composite models, also taking into account the dependence between attritional and large claims.

Secure and Effective Key Management Using Secret Sharing Schemes in Cloud Computing

Security is a crucial problem in Cloud computing. Storing and accessing the data in the Cloud is very popular nowadays but the security of data is still lagging behind. Secret sharing schemes are widely used to improve the security of data. In this article, a threshold secret sharing scheme using Newton divided difference interpolating polynomial (TSSNIP) is proposed in a distributed Cloud environment to enhance security of keys used for encryption. The proposed method uses a Newton divided difference interpolating polynomial for key splitting and key reconstruction. A threshold value is used to reconstruct the shares in secret sharing schemes. The proposed work made use of dynamic and random threshold generation method to ensure security of key. The experimental output shows reduced execution time, better security, efficiency, and robustness in the proposed scheme. Furthermore, the proposed scheme also outperformed other secret sharing schemes.

Mechanism Design With Aftermarkets: Cutoff Mechanisms

I study a mechanism design problem in which a designer allocates a single good to one of several agents, and the mechanism is followed by an aftermarket—a post‐mechanism game played between the agent who acquired the good and third‐party market participants. The designer has preferences over final outcomes, but she cannot design the aftermarket. However, she can influence its information structure by publicly disclosing information elicited from the agents by the mechanism. I introduce a class of allocation and disclosure rules, called cutoff rules, that disclose information about the buyer's type only by revealing information about the realization of a random threshold (cutoff) that she had to outbid to win the object. When there is a single agent in the mechanism, I show that the optimal cutoff mechanism offers full privacy to the agent. In contrast, when there are multiple agents, the optimal cutoff mechanism may disclose information about the winner's type; I provide sufficient conditions for optimality of simple designs. I also characterize aftermarkets for which restricting attention to cutoff mechanisms is without loss of generality in a subclass of all feasible mechanisms satisfying additional conditions.

Explore/exploit tradeoff strategies in a resource accumulation search task

How, and how well, do people switch between exploration and exploitation to search for and accumulate resources? We study the decision processes underlying such exploration/exploitation tradeoffs by using a novel card selection task. With experience, participants learn to switch appropriately between exploration and exploitation and approach optimal performance. We model participants’ behavior on this task with random, threshold, and sampling strategies, and find that a linear decreasing threshold rule best fits participants’ results. Further evidence that participants use decreasing threshold-based strategies comes from reaction time differences between exploration and exploitation; however, participants themselves report non-decreasing thresholds. Decreasing threshold strategies that “front-load” exploration and switch quickly to exploitation are particularly effective in resource accumulation tasks, in contrast to optimal stopping problems like the Secretary Problem requiring longer exploration.