Description: The April 2016 Issue of The Modeling Platform

Description: For the purposes of risk management and stress testing, we characterize a spectrum of plausible extreme events, that we dub Grey Swans, by introducing a probabilistic method involving the concentration of measure phenomenon. As a result, stress tests can be triaged according to severity, probability and, now, information-based plausibility.

Description: Target volatility, as an investment strategy, has been used by many funds not only as a way to smooth return but also as a way to manage risks of the VA guarantees. Contrary to existing research which favors volatility targeting over fixed allocation, this article shows that because of the existence of market jump risk, target volatility strategy does not necessarily offer an improved risk profile for VA writers. Further, target volatility coupled with leveraging can significantly increase tail risk for insurance companies.

Description: For the purposes of risk management and stress testing, we characterize a spectrum of plausible extreme events, that we dub Grey Swans, by introducing a probabilistic method involving the concentration of measure phenomenon. As a result, stress tests can be triaged according to severity, probability and, now, information-based plausibility.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.