Seismic Risk Management of Insurance Portfolio Using Catastrophe Bonds

Goda, Katsuichiro

doi:10.1111/mice.12093

Cited by 22 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PBEE aims to quantify the extent of damage and consequences probabilistically and is useful for assessing financial and socioeconomic impact of earthquake-related hazards (e.g. Goda 2015). Therefore, the presented results can be viewed from a Fig.…”

Section: Tsunami Loss Assessmentmentioning

confidence: 99%

“…In the last two decades, tsunamis triggered by earthquakes were responsible for 33 % of total deaths and 35 % of total economic losses globally (Guha-Sapir et al 2015). A reliable quantification of tsunami risk becomes increasingly important for emergency officers to manage critical infrastructures and for insurance companies to quantify the expected economic losses (Goda 2015). An accurate risk analysis encompasses reliable assessments of hazard, exposure, and vulnerability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian tsunami fragility modeling considering input data uncertainty

Risi

Goda

Mori

et al. 2016

Stoch Environ Res Risk Assess

Self Cite

View full text Add to dashboard Cite

Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, are considered, and are applied to extensive tsunami damage data for the 2011 Tohoku earthquake. A unique aspect of this study is that uncertainty of tsunami inundation data (i.e. input hazard data in fragility modeling) is quantified by comparing two tsunami inundation/run-up datasets (one by the Ministry of Land, Infrastructure, and Transportation of the Japanese Government and the other by the Tohoku Tsunami Joint Survey group) and is then propagated through Bayesian statistical methods to assess the effects on the tsunami fragility models. The systematic implementation of the data and methods facilitates the quantitative comparison of tsunami fragility models under different assumptions. Such comparison shows that the binomial logistic method with un-binned data is preferred among the considered models; nevertheless, further investigations related to multinomial logistic regression with un-binned data are required. Finally, the developed tsunami fragility functions are integrated with building damage-loss models to investigate the influences of different tsunami fragility curves on tsunami loss estimation. Numerical results indicate that the uncertainty of input tsunami data is not negligible (coefficient of variation of 0.25) and that neglecting the input data uncertainty leads to overestimation of the model uncertainty.

show abstract

Section: Tsunami Loss Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian tsunami fragility modeling considering input data uncertainty

Risi

Goda

Mori

et al. 2016

Stoch Environ Res Risk Assess

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are only three dummy variables instead of four to avoid over-parametrisation. The regression parameters for depth-based fragility according to Equation (6) are shown in Table 1, and Figure 2 shows corresponding fragility curves, noting that this fragility model was referred to as M3 in De Risi et al [37]. Regression parameters for bivariate-IM fragility according to Equation (7) for plain coast and ria coast are listed in Table 2; this fragility function was referred to as M4 in De Risi et al [37].…”

Section: Bivariate-im Tsunami Loss Estimationmentioning

confidence: 99%

“…Catastrophe risk assessment is essential for achieving effective risk management to deal with the low-probability high-consequence catastrophes in the global insurance-reinsurance system by transferring financial risks among stakeholders [1][2][3]. Tsunamis are one of the low-probability high-consequence natural disasters, and thus the improved accuracy of tsunami loss estimation can help insurance/re-insurance underwriters to better understand their exposure to catastrophe risks and is beneficial for profitable design of risk transfer instruments [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Influence of Flow Velocity on Tsunami Loss Estimation

2017

Self Cite

View full text Add to dashboard Cite

Inundation depth is commonly used as an intensity measure in tsunami fragility analysis. However, inundation depth cannot be taken as the sole representation of tsunami impact on structures, especially when structural damage is caused by hydrodynamic and debris impact forces that are mainly determined by flow velocity. To reflect the influence of flow velocity in addition to inundation depth in tsunami risk assessment, a tsunami loss estimation method that adopts both inundation depth and flow velocity (i.e., bivariate intensity measures) in evaluating tsunami damage is developed. To consider a wide range of possible tsunami inundation scenarios, Monte Carlo-based tsunami simulations are performed using stochastic earthquake slip distributions derived from a spectral synthesis method and probabilistic scaling relationships of earthquake source parameters. By focusing on Sendai (plain coast) and Onagawa (ria coast) in the Miyagi Prefecture of Japan in a case study, the stochastic tsunami loss is evaluated by total economic loss and its spatial distribution at different scales. The results indicate that tsunami loss prediction is highly sensitive to modelling resolution and inclusion of flow velocity for buildings located less than 1 km from the sea for Sendai and Onagawa of Miyagi Prefecture.

show abstract

“…Some notable applications have included: modeling of tropical cyclones (Daneshvaran and Morden 2004), systemic risks in agriculture for the case of Georgia cotton (Vedenov et al 2006), transportation assets and feasibility analysis for bridges (Sircar et al 2009), calibration using Chinese earthquake loss data (Wu and Zhou 2010), models for earthquakes (Penalva Zuast 2002;Zimbidis et al 2007;Tao et al 2009;Härdle and Cabrera 2010;Ahrens et al 2014;Shao et al 2015), modeling of tornado occurrence in the USA (Hainaut and Boucher 2014), exposure to currency exchange risk (Lai et al 2014), seismic risk management of insurance portfolio (Goda 2015), hedging of flood losses (Tetu et al 2015), and temperature-based agricultural applications (Karagiannis et al 2016) among others.…”

mentioning

confidence: 99%