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The impact of ground motion prediction equations on estimating insurance losses in the EQC at earthquake catastrophe model
Published in Chongfu Huang, Zoe Nivolianitou, Risk Analysis Based on Data and Crisis Response Beyond Knowledge, 2019
Zhenghui Xiong, Zhijun Dai, Xiaojun Li*
Catastrophe modeling is a significant tool for assessing insurance losses in horrible disasters such as earthquakes, floods and hurricanes. As an effective means of risk management, insurance plays an important role in mitigating natural disaster losses and helping recovery and reconstruction post-disaster. Insurance and reinsurance operations commonly rely on large sets of historical loss experience data to conduct their business, set premiums, and to conceive their risk management strategy. However, for operations concerned with less frequent perils but huge losses such as earthquakes, the data historically available is insufficient to obtain reliable risk statistics (Franco, 2012). The industry makes the use of numerical models that, based on the current understanding of the mechanisms underlying environmental hazards and supported by existing data, estimate the probability of occurrence as well as the consequences of catastrophic events in terms of monetary loss (Grossi & Kunreuther, 2005). These models are known as catastrophe models.
Port Security as a Risk Management Activity
Published in Kenneth Christopher, Port Security Management, 2014
Catastrophe modeling uses computer technology in assessing potential losses that may occur given different threat scenarios. For example, the insurance industry’s use of catastrophe modeling in managing risks associated with natural disasters draws from a diverse spectrum of disciplines such as decision sciences, meteorology, and seismology to match historical disaster information with current demographic, building (age, type, and usage), scientific, and financial data to determine the potential cost of catastrophes for a specified geographic area. The models use these vast databases of information to simulate the physical characteristics of thousands of potential catastrophes and project their effects on both residential and commercial properties (Insurance Information Institute 2007, par. 1).
Hurricane risk analysis of the residential structures located in Florida
Published in Sustainable and Resilient Infrastructure, 2020
Grzegorz Kakareko, Sungmoon Jung, O. Arda Vanli
The main objective in catastrophe modeling (cat models) is to quantify the probability of occurrence and severity of a loss (AIR Worldwide, 2015; Applied Research Associates, I., Division, I., 2015; CoreLogic EQECAT, 2015; RiskLink 15.0, 2015). Based on catastrophe analysis insurance industry and the governments can prepare for the eventual future loss. In this research, we address the probability and the severity of the event by the annual exceedance probability, which quantifies the loss with the certain probability of occurrence in a year. The return period has an inverse relation to the exceedance probability: e.g., 100 years return period means that the even has 1% chance of occurrence in one year. A 100-year event may occur more than once in 100 years. Different approaches are available in the literature to quantify occurrence probability of an event (Elsner et al., 2008; Powell et al., 2005). In this research, we used the most common approach based on the binomial distribution (Equation (4)). Equation (4) predicts the probability of occurrence of an event () with the expected return period () in the next years.