A wide variety of models, methods and tools exist to assess the impacts and risks of disasters beyond the direct destruction of structures. Research on the societal dimensions of earthquakes and other perils has become a deeply interdisciplinary science and much of the research has been focused on the design of models which explain social vulnerability and the root causes which create it (Tierney, 2010; Wisner et al., 2004; Blaikie et al., 1994). However, work remains on “operationalization”, which is critical for translating conceptual frameworks into practical tools for measuring and communicating societal impacts of disasters.
Indicator-based approaches are increasingly being used to measure social and economic impacts across regions, countries, and populations. State-of-the-art indicator systems are attempting to describe how vulnerability can be captured in its different dimensions (e.g., physical susceptibility, social-cultural issues and socio-economic contexts). In addition, some indicator frameworks depict/measure how vulnerability is influenced by resilience; i.e. the adaptive ability of a socio-economic system to absorb negative impacts as result of its capacity to anticipate, cope and recover quickly from damaging events. Read more about Socio-Economic Impact.
Indirect losses, Business Interruption, Contingent Business Interruption & Supply Chain Risk
The Asia Pacific natural catastrophes of 2011 highlighted the global extent of business interruption and supply chain risk from national scale natural disasters. The insurance sector has provided coverage for business interruption for many years and the need to model exposures more accurately has coincided with increased demand for these types of risk management products.
Contingent business interruption (CBI) had been largely excluded from general insurance policies but has recently come into regional and global policies herewith increasing losses. CBI has become the source for indirect losses for recent events such as the Tohoku (2011) earthquake and the Thailand flood (2011). CBI losses have started to globalize risk due to the global effect of supply chain interruption.
Willis has created a new WRN Hub with scientists from the CEDIM group, Pavia, and UCL, among others to drive SEI, BI and CBI research and product for our industry.
Infrastructure & Utilities
The impact of natural hazards on infrastructure, utilities and related networks has a significant influence on economic impacts and civil protection. The relative resilience of infrastructure multiplies or dampens the effects of natural disasters. The lifespan of infrastructure investments, coupled with the importance of utility services renders them among the first sectors to be directly affected by major events.
WRN is funding two extended PhD positions on infrastructure risk and modeling at UCL and has been working with universities such as Kyoto University and Berkeley and UCL to create platforms and tools to better understand infrastructure risk and the potential failure of infrastructure risk in large natural events. The aim is to model infrastructure losses from the bottom up as well as top down i.e. considering various and partly complex interaction of different levels of risk, as well as indicators in order to approximate losses for areas where available data is incomplete and insufficient for direct and detailed loss modeling.
The insurance sector experiences the impact of natural disasters on the significant increase in like-for-like repair costs and insurance claims after major events through a process known as demand surge or loss amplification. These phenomena can increase claim costs by more than 30% as labour and material costs rise in the wake of increased demands. Post event Demand surge is depended upon a range of specific factors related to the hazard and exposure as well as the state of the economy. WRN undertakes leading research to underpin demand surge risk modeling as well as in order to include further variables such as those describing the state of the economy to approximate demand surge most accurately. This research has been done with Keith Porter from the Colorado University.
Date: Sep 03, 2012 | Type: Article |
Journal: Journal of Econometrics | Ext. Link: Click Here ›
Authors: Jin-Chuan Duan, Jie Sun, Tao Wang
Summary: In this paper a forward intensity model for the prediction of corporate defaults over different future periods is proposed.