Disaster Risk Models Research Articles

Time-Varying Disaster Risk Models: An Empirical Assessment of the Rietz-Barro Hypothesis

This paper revisits the t of disaster risk models where a representative agent has recursive preferences and the probability of a macroeconomic disaster changes over time. We calibrate the model as in Wachter (2013) and perform two sets of tests to assess the empirical performance of the model in long run simulations. The model is solved using a two step projection-based method that allows us to nd the equilibrium consumption-wealth ratio and dividend-yield for dierent values of the intertemporal elasticity of substitution. By xing the elasticity of substitution to one, the rst experiment indicates that the overall t of the model is adequate. However, we nd that the amount of aggregate stock market volatility that the model can generate is sensible to the method used to solve the model. We also nd that the model generates near unit root interest rates and a puzzling ranking of volatilities between the risk free rate and the expected return on government bills. We later solve the model for values of the elasticity of substitution that dier from one. This second experiment shows that while a higher elasticity of substitution helps to increase the aggregate stock market volatility and hence to reduce the Sharpe Ratio, a lower elasticity of substitution generates a more reasonable level for the equity risk premium and for the volatility of the government bond returns without compromising the ability of the price-dividend ratio to predict excess returns.

Climate Change Induced Flood Risk and Adaptation in the Padma River Island, Bangladesh: A Local Scale Approach

Assessment of hazard characteristics, exposure, vulnerability, resilience and risk at a local scale is an important tool for identifying the capacities of households and local communities to adapt with climate change induced flood. Repeated floods with different magnitudes under variable climatic conditions are great catastrophe for the people living in flood prone regions specifically in Islands. People, infrastructure and economy are highly exposed to flood and that results in devastating flood disaster. In the Padma river Islands frequent flooding makes the life and livelihoods of people vulnerable and this vulnerability is conditioned by different factors such as people’s physical, social, economic and environmental condition. The higher is the level of vulnerability the higher is the level of flood risk. Hence, people’s resilience can minimize the negative impacts of flood disaster. A composite risk index (related to the probability of occurrence of the event, magnitude of the event, quantity and cost of the element at risk, vulnerability and resilience) was prepared to realize the climate change induced flood disaster risk. Some adaptive strategies are explored to adapt with climate change induced flood risk. Finally, Local Scale Disaster Risk Model (LSDR) was proposed to present the framework of climate change induced flood disaster risk and adaption at a local scale. DOI: http://dx.doi.org/10.3329/jles.v8i0.20138 J. Life Earth Sci., Vol. 8: 41-48, 2013

Spatial modelling of natural disaster risk reduction policies with Markov decision processes

The 2010/2011 floods in Queensland, Australia inflicted significant damages to government's critical infrastructures, private properties and businesses reaching an estimated amount of AU$16 billion. Mitigating the devastating effects of floods to the community and critical infrastructures entails competing financial requirements at the different levels of government. Hence, the main objective of this study was to examine the financial optimality of disaster risk reduction measures by integrating Markov decision processes (MDP for short) with geographic information system (GIS). Conducted in the core suburbs of Brisbane City, we organised the MDP variables using the following: 1) flood risk levels as the states of the urban system; 2) Queensland's disaster risk reduction measures as the action variables; 3) percentage of government expenditures by disaster risk reduction category as the state transition probabilities; 4) total lost earnings to businesses affected by the flood events as the reward variables; and 5) the weighted average riskless rate of return, the weighted average rate of return, and rate of return for a riskier asset as discounting factors. We analysed 36 MDP scenarios at four-level iteration and then calculated the expectimax values to find the optimal policy. The results from the analyses revealed that the Commonwealth government optimised the use of its natural disaster risk reduction expenditures to recovery while the State government focused on mitigation. When both government expenditures combined, the mitigation measure was identified as the optimum natural disaster risk reduction policy. The methodology presented in this study allowed a spatial representation and computationally feasible integration of complex flood disaster risk model with government expenditures and business earnings. The insights from this integrated approach emphasise the viability of finding optimum expenditures, and re-examine if necessary, in implementing natural disaster risk reduction policies and climate adaptation strategies.

백두산 화산 분화 시나리오에 따른 경제적 손실 평가

백두산 화산의 분화 가능성이 높아지면서, 백두산 화산 분화가 한반도를 비롯하여 지구촌에 미칠 사회경제적 영향에 대한 관심도 높아지고 있다. 백두산 화산이 분화할 경우 남한지역은 특히 화산재로 인한 직접 피해를 입게 되며, 이로 인한 2차 피해가 다양한 형태로 나타날 수 있다. 이에 이 연구는 백두산 화산 분화의 사회경제적 영향 평가에 있어 기초가 되는 직접 피해액 계산 방법을 고찰해 보고, 백두산 화산 분화 시나리오의 직접 피해액을 산정해 보고자 하였다. 직접 피해액 산정 방법으로는 재난 리스크 모델링 기술을 활용하였다. 현재까지 연구 결과에 의하면, 백두산 화산 분화 시 강원도 지역을 중심으로 화산재 피해를 입게 된다. 이에 강원도 지역을 중심으로 피해목적물 정보를 구축하였으며, 특히 강원도 전체 면적의 90% 이상을 차지하는 농업과 임업 분야를 중심으로 피해목적물 정보를 구축하고, 피해목적물별 손상함수를 정리하였다. 백두산 화산 분화 시나리오에 따른 화산재 두께 분포와 피해목적물의 공간 정보 및 자산 정보, 그리고 손상함수를 활용하여 피해액을 산출한 결과, 이 연구에서 참고한 겨울철 시나리오의 경우, 농업과 임업분야에서 각각 2,998억원(2010년 생산액의 20.4%)과 289억원(2010년 생산액의 9.0%)의 피해를 입는 것으로 나타났다. 농업분야에서 더 큰 피해를 입는 것을 알 수 있는데, 이 연구에 사용된 손상함수에 따르면, 농산물이 임산물보다 화산재에 더 취약하기 때문이다. 또한 강원도 지역은 농산물 생산액(2010년 기준 14,717억원)이 임산물 생산액(2010년 기준 3,223억원)보다 4.5배 이상 크다. 겨울철 시나리오의 경우, 피해액이 가장 큰 지역은 인제군, 강릉시 등으로 인제군은 비록 생산액은 많지 않으나 화산재 두께가 높이 쌓여(평균 8.5 mm) 큰 피해를 입힌 것으로 나타났다. 반면 고성군의 경우 20 mm 이상의 화산재 두께가 쌓이나 생산액이 적어 비교적 적은 피해를 보였다. 이와같이 산정된 직접 피해액은 투입산출모형과 같은 간접피해평가모형을 통해 간접 피해를 계산하는데 활용될 수 있다. As Mt. Backdu is expected to erupt, the social and economic impacts of the eruption on the Korean peninsula as well as on the world become a research topic of interest. If the volcano erupts, South Korea can be directly impacted by volcanic ash, which will bring out secondary damages in various ways. Given that the direct damage is a basis to estimate indirect and secondary damages, this paper was to review a method to estimate direct damages, called catastrophe risk models, and estimate the direct damages of available eruption scenarios of Mt. Baekdu. Based on the results, the damages by volcanic ash will occur mostly around Gangwon province if the Mt. Backdu erupts. Thus the inventory lists and their damage functions of Gangwon provinces were collected. In particular agricultural and forestry products were surveyed based on the land use. Direct damages were estimated using volcanic ash distribution of eruption scenarios, inventory information and their damage functions. In result, a scenario in winter caused the damage of 299.8 billion KRW (20.4% of total agricultural production in 2010) and 28.9 billion KRW (9.0% of total forestry production in 2010) in agriculture and forestry, respectively. The damages in agriculture was larger, and it is due to the damage functions which show the agricultural products are more vulnerable to volcanic ash than forestry products. Also the agricultural production (1,471.7 billion KRW in 2010) are more than 4.5 times the forestry production (322.3 billion KRW in 2010) in Gangwon province. Inje and Gangnung had the most damages in the scenario in winter. Inje had the most damage due to the thick ash deposit (8.5 mm in average) despite the low production. On the other hand, Goseong had a low damage compared to the ash thickness larger than 20mm, owing to the low production. The direct damage estimated through this process can be used to estimate indirect damages.

The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model

Abstract. Catastrophe risk models used by the insurance industry are likely subject to significant uncertainty, but due to their proprietary nature and strict licensing conditions they are not available for experimentation. In addition, even if such experiments were conducted, these would not be repeatable by other researchers because commercial confidentiality issues prevent the details of proprietary catastrophe model structures from being described in public domain documents. However, such experimentation is urgently required to improve decision making in both insurance and reinsurance markets. In this paper we therefore construct our own catastrophe risk model for flooding in Dublin, Ireland, in order to assess the impact of typical precipitation data uncertainty on loss predictions. As we consider only a city region rather than a whole territory and have access to detailed data and computing resources typically unavailable to industry modellers, our model is significantly more detailed than most commercial products. The model consists of four components, a stochastic rainfall module, a hydrological and hydraulic flood hazard module, a vulnerability module, and a financial loss module. Using these we undertake a series of simulations to test the impact of driving the stochastic event generator with four different rainfall data sets: ground gauge data, gauge-corrected rainfall radar, meteorological reanalysis data (European Centre for Medium-Range Weather Forecasts Reanalysis-Interim; ERA-Interim) and a satellite rainfall product (The Climate Prediction Center morphing method; CMORPH). Catastrophe models are unusual because they use the upper three components of the modelling chain to generate a large synthetic database of unobserved and severe loss-driving events for which estimated losses are calculated. We find the loss estimates to be more sensitive to uncertainties propagated from the driving precipitation data sets than to other uncertainties in the hazard and vulnerability modules, suggesting that the range of uncertainty within catastrophe model structures may be greater than commonly believed.

Modeling catastrophic deaths using EVT with a microsimulation approach to reinsurance pricing

Recently, a marked Poisson process (MPP) model for life catastrophe risk was proposed in Ekheden & Hössjer (2014). We provide a justification and further support for the model by considering more general Poisson point processes in the context of extreme value theory (EVT), and basing the choice of model on statistical tests and model comparisons. A case study examining accidental deaths in the Finnish population is provided. We further extend the applicability of the catastrophe risk model by considering small and big accidents separately; the resulting combined MPP model can flexibly capture the whole range of accidental death counts. Using the proposed model, we present a simulation framework for pricing (life) catastrophe reinsurance, based on modeling the underlying policies at individual contract level. The accidents are first simulated at population level, and their effect on a specific insurance company is then determined by explicitly simulating the resulting insured deaths. The proposed microsimulation approach can potentially lead to more accurate results than the traditional methods, and to a better view of risk, as it can make use of all the information available to the re/insurer and can explicitly accommodate even complex re/insurance terms and product features. As an example, we price several excess reinsurance contracts. The proposed simulation model is also suitable for solvency assessment.

가용자료가 제한된 경우 화산재 피해 예측을 위한 손상함수 구축

Catastrophe risk models require the damage functions of each vulnerable item in inventory to estimate volcanic ash losses. The damage functions are used to represent the relation between damage factors and damage and also widely used in engineering and natural hazard studies to calculate the vulnerability. In most cases, damage functions are constructed as fragility or vulnerability curves, and researchers are confused by the similarities between them particularly when they perform interdisciplinary research. Thus, we aim to explain the similarities and differences between fragility and vulnerability curves and their relationship by providing case studies to construct them. In addition, we suggest a simple method to construct the damage functions between damage ratio and volcanic ash thickness using limited damage data. This study comes from the fact that damage functions are generally constructed using damage data. However, there is no available volcanic ash damage data in Korea, and not even enough volcanic disaster data to construct damage functions in the world, compared to other hazards. Using the method suggested in the study and the limited damage data from Japan and New Zealand, we construct Weibull-type functions or linear functions dependent of available data to calculate volcanic ash loss estimation, which we think need to be corrected to make it more suitable for inventory characteristics and environmental conditions in Korea.

Community-based disaster risk and vulnerability models of a coastal municipality in Bangladesh

Bangladesh is one of the most natural hazard-prone countries in the world with the greatest negative consequences being associated with cyclones, devastating floods, riverbank erosion, drought, earthquake, and arsenic contamination, etc. One way or other, these natural hazards engulfed every corner of Bangladesh. The main aim of this research paper is to carry out a multi-hazards risk and vulnerability assessment for the coastal Matlab municipality in Bangladesh and to recommend possible mitigation measures. To this aim, hazards are prioritized by integrating SMUG and FEMA models, and a participation process is implemented so as to involve community both in the risk assessment and in the identification of adaptation strategies. The Matlab municipality is highly vulnerable to several natural hazards such as cyclones, floods, and riverbank erosion. The SMUG is a qualitative assessment, while FEMA is a quantitative assessment of hazards. The FEMA model suggests a threshold of highest 100 points. All hazards that total more than 100 points may receive higher priority in emergency preparedness and mitigation measures. The FEMA model, because it judges each hazard individually in a numerical manner, may provide more satisfying results than the SMUG system. The spatial distributions of hazard, risk, social institutions, land use, and other resources indicate that the flood disaster is the top environmental problem of Matlab municipality. Hazard-specific probable mitigation measures are recommended with the discussion of local community. Finally, this study tries to provide insights into the way field research combining scientific assessments tools such as SMUG and FEMA could feed evidence-based decision-making processes for mitigation in vulnerable communities.

Catastrophe Risk Models for Evaluating Disaster Risk Reduction Investments in Developing Countries

Major natural disasters in recent years have had high human and economic costs, and triggered record high postdisaster relief from governments and international donors. Given the current economic situation worldwide, selecting the most effective disaster risk reduction (DRR) measures is critical. This is especially the case for low- and middle-income countries, which have suffered disproportionally more economic and human losses from disasters. This article discusses a methodology that makes use of advanced probabilistic catastrophe models to estimate benefits of DRR measures. We apply such newly developed models to generate estimates for hurricane risk on residential structures on the island of St. Lucia, and earthquake risk on residential structures in Istanbul, Turkey, as two illustrative case studies. The costs and economic benefits for selected risk reduction measures are estimated taking account of hazard, exposure, and vulnerability. We conclude by emphasizing the advantages and challenges of catastrophe model-based cost-benefit analyses for DRR in developing countries.

Robust Economic Implications of Nonlinear Pricing Kernels

Based on a family of discrepancy functions, we derive nonparametric stochastic discount factor (SDFs) bounds that naturally generalize variance (Hansen and Jagannathan, 1991), entropy (Backus, Chernov and Martin, 2011), and higher-moment (Snow, 1991) bounds. These bounds are especially useful to identify how parameters affect pricing kernel dispersion in asset pricing models. In particular, they allow us to distinguish between models where dispersion comes mainly from skewness from models where kurtosis is the primary source of dispersion. We analyze the admissibility of disaster, disappointment aversion and long-run risk models with respect to these bounds.

Design and Development of Flood/Waterlogging Disaster Risk Model Based on ArcObjects

Design and Development of Flood/Waterlogging Disaster Risk Model Based on ArcObjects

Catastrophe risk models as a new tool to investigate the financial risks associated with climate change impacts and cost-benefits of adaptation

This article was submitted without an abstract, please refer to the full-text PDF file.

Catastrophe risk models as a new tool to investigate the financial risks associated with climate change impacts and cost-benefits of adaptation

Catastrophe risk models as a new tool to investigate the financial risks associated with climate change impacts and cost-benefits of adaptation

Prospective Longevity Risk Analysis

ABSTRACTMortality improvement has traditionally been analysed using an array of statistical methods, and extrapolated to make actuarial projections. This paper presents a forward-looking approach to longevity risk analysis which is based on stochastic modelling of the underlying causes of mortality improvement, due to changes in lifestyle, health environment, and advances in medical science. The rationale for this approach is similar to that adopted for modelling other types of dynamic insurance risk, e.g. natural catastrophes, where risk analysts construct a stochastic ensemble of events that might happen in the future, rather than rely on a retrospective analysis of the non-stationary and comparatively brief historical record.Another feature of prospective longevity risk analysis, which is shared with catastrophe risk modelling, is the objective of capturing vulnerability data at a high resolution, to maximise the benefit of detailed modelling capability down to individual risk factor level. Already, the use by insurers of postcode data for U.K. flood risk assessment has carried over to U.K. mortality assessment. Powered by fast numerical computation and parameterised with high quality geographical data, hydrological models of flood risk have superseded the traditional statistical insurance loss models. A decade later, medically-motivated computational models of mortality risk can be expected to gain increasing prominence in longevity risk management.

Economic Implications of Nonlinear Pricing Kernels

Based on a family of discrepancy functions, we derive nonparametric stochastic discount factor bounds that naturally generalize variance, entropy, and higher-moment bounds. These bounds are especially useful to identify how parameters affect pricing kernel dispersion in asset pricing models. In particular, they allow us to distinguish between models where dispersion comes mainly from skewness from models where kurtosis is the primary source of dispersion. We analyze the admissibility of disaster, disappointment aversion, and long-run risk models with respect to these bounds.This paper was accepted by Jerome Detemple, finance.

Catastrophe Risk Models for Wildfires in the Wildland–Urban Interface: What Insurers Need

Insurers use catastrophe risk models to estimate loss exceedance probabilities for a range of different hazard events. The 2003 wildfires in Southern California caused over $2 billion in insured losses and increased insurer’s interest in developing risk models for wildfire in the wildland–urban interface (WUI). The three main parts of a catastrophe risk model are a hazard module, a damage module, and a loss module. The discussion below focuses on improving the hazard module of a catastrophe risk model for wildfire in the WUI with a particular emphasis on the southwest California. A database of insured and total losses from wildfire that is normalized for the effects of inflation and for changes in population and property value within the WUI is needed for calibrating model losses. Variations in the probability of fire occurrence should account for historical changes in fire suppression practices and the influence of climate variability. A property has an excellent chance of resisting ignition if flame-res...

Modeling Catastrophes and their Impact on Insurance Portfolios

The authors propose a general individual catastrophe risk model that allows damage ratios to be random functions of the catastrophe intensity. They derive some distributional properties of the insured risks and of the aggregate catastrophic loss under this model. Through the model and ruin probability calculations, they formally illustrate the well-known fact that the catastrophe risk cannot be diversified through premium collection alone, as is the case with the usual “day-to-day” risk, even for an arbitrary large portfolio. They also derive some risk orderings between different catastrophe portfolios and show that the risk level of a realistic portfolio falls between that of a portfolio of comonotonic risks and that of a portfolio of independent risks. Finally, the authors illustrate their findings with a numerical example inspired from earthquake insurance.

The role of the pediatrician in the adolescent's school: Duke, P. M. Dept. Ped., Stanford Univ. Sch. Med., Stanford, Calif., U.S.A. Pediatr. Clin. North Am. 27(1), 163–171, 1980

Disaster risk management (DRM) seeks to help societies prepare for, mitigate, or recover from the adverse impacts of disasters and climate change. Core to DRM are disaster risk models that rely heavily on geospatial data about the natural and built environments. Developers are increasingly turning to artificial intelligence (AI) to improve the quality of these models. Yet, there is still little understanding of how the extent of hidden geospatial biases affects disaster risk models and how accountability relationships are affected by these emerging actors and methods. In many cases, there is also a disconnect between the algorithm designers and the communities where the research is conducted or algorithms are implemented. This perspective highlights emerging concerns about the use of AI in DRM. We discuss potential concerns and illustrate what must be considered from a data science, ethical, and social perspective to ensure the responsible usage of AI in this field.