Optimal Exercise Strategies for Operational Risk Insurance via Multiple Stopping Times

In this paper we demonstrate how to develop analytic closed form solutions to optimal multiple stopping time problems arising in the setting in which the value function acts on a compound process that is modified by the actions taken at the stopping times. This class of problem is particularly relevant in insurance and risk management settings and we demonstrate this on an important application domain based on insurance strategies in Operational Risk management for financial institutions. In this area of risk management the most prevalent class of loss process models is the Loss Distribution Approach (LDA) framework which involves modelling annual losses via a compound process. Given an LDA model framework, we consider Operational Risk insurance products that mitigate the risk for such loss processes and may reduce capital requirements. In particular, we consider insurance products that grant the policy holder the right to insure k of its annual Operational losses in a horizon of T years. We consider two insurance product structures and two general model settings, the first are families of relevant LDA loss models that we can obtain closed form optimal stopping rules for under each generic insurance mitigation structure and then secondly classes of LDA models for which we can develop closed form approximations of the optimal stopping rules. In particular, for losses following a compound Poisson process with jump size given by an Inverse-Gaussian distribution and two generic types of insurance mitigation, we are able to derive analytic expressions for the loss process modified by the insurance application, as well as closed form solutions for the optimal multiple stopping rules in discrete time (annually). When the combination of insurance mitigation and jump size distribution does not lead to tractable stopping rules we develop a principled class of closed form approximations to the optimal decision rule. These approximations are developed based on a class of orthogonal Askey polynomial series basis expansion representations of the annual loss compound process distribution and functions of this annual loss.     

Radiolabeled GX1 Peptide for Tumor Angiogenesis Imaging

Early and accurate detection of primary or metastatic tumors is of great value in staging, treatment management, and prognosis. Tumor angiogenesis plays an essential role in the growth, invasion, and metastatic spread of solid cancers, and so, is a promising approach for tumor imaging. The GX1 (CGNSNPKSC) peptide was identified by phage display library and has been investigated as a marker for human cancers. This study aims to evaluate the ^99mTc-HYNIC-PEG₄-c (GX1) as a biomarker for tumor imaging. Our results showed that GX1 specifically binds to tumor cells in vitro. SKMEL28 and MDA-MB231 cells achieved total binding peak at 60 min of incubation. For B16F10 and MKN45 cells, the total and specific binding were similar during all time points, while A549 cell line showed rapid cellular total uptake of the tracer at 30 min of incubation. Biodistribution showed low non-specific uptakes and rapid renal excretion. Melanoma tumors showed enhanced GX1 uptake in animal model at 60 min, and it was significantly blocked by cold peptide. The radiotracer showed tumor specificity, especially in melanomas that are highly vascularized tumors. In this sense, it should be considered in future studies, aiming to evaluate degree of angiogenesis, progression, and invasion of tumors.     

Antimicrobial activity of silver nanoparticle colloids of different sizes and shapes against Streptococcus mutans

Research on Chemical Intermediates - Silver nanoparticles stabilized with chitosan biopolymer are a new antibacterial agent for treatment of caries. To determine whether the size and morphology of...