We study the suitability of average peak ground acceleration (PGA) as a ground-motion proxy for parametric catastrophe bond (CAT bond) design. We tie the selection of PGA (as a triggering parameter for CAT bonds) to computational convenience (fast retrieval from the recorded ground motion) and loss correlation (optimum monetary return on the investor side). Our case studies advocate that PGA, as a candidate ground-motion proxy, can be used confidently for parametric CAT bonds, particularly applications associated with dense coverage of seismic networks. It is still a compelling ground-motion proxy even if the seismic network coverage is sparse, provided that the accelerometric stations are deployed in the vicinity of assets that financially represent the most significant portion of the insured building stock. We establish an event-based risk model of the Istanbul city (via Monte Carlo simulations) to depict the rationale behind our proposition and compare its performance with other competing (more sophisticated) proxies in terms of accelerometric network density and spatial distribution as well as the different risk levels used in risk management.
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References
1.
AhmadN., CrowleyH., and PinhoR., 2010. Analytical Fragility Functions for Reinforced Concrete and Masonry Buildings and Building Aggregates of Euro-Mediterranean Regions – UPAV Methodology, Internal Report, Syner-G Project 2009/2012, European Commission 7th Framework Programme.
2.
AkkarS., and ChengY., 2016. Application of a Monte-Carlo simulation approach for the probabilistic assessment of seismic hazard for geographically distributed portfolio, Earthquake Engineering and Structural Dynamics45, 525–541.
3.
AkkarS., and ÖzenÖ., 2005. Effect of peak ground velocity on deformation demands for SDOF systems, Earthquake Engineering and Structural Dynamics34, 1551–1571.
4.
AkkarS., SandikkayaM. A., and BommerJ. J., 2014. Empirical ground-motion models for point- and extended-source crustal earthquake scenarios in Europe and the Middle East, Bulletin of Earthquake Engineering12, 359–387.
5.
AndersonJ. G., and LucoJ. E., 1983. Consequences of slip rate constraints on earthquake occurrence relations, Bulletin of the Seismological Society of America73, 471–496.
6.
Artemis, 2018. Catastrophe bonds and insurance-linked securities deal directory, available at http://www.artemis.bm/deal_directory/ (last accessed 22 June 2018).
7.
BalI. E., CrowleyH., PinhoR., and GülayF. G., 2008. Detailed assessment of structural characteristics of Turkish RC building stock for loss assessment models, Soil Dynamics and Earthquake Engineering28, 914–932.
8.
BooreD. M., GibbsJ. F., JoynerW. B., TinsleyJ. C., and PontiD. J., 2003. Estimated ground motion from the 1994 Northridge, California, earthquake at the site of the Interstate 10 and La Cienega Boulevard bridge collapse, West Los Angeles, California, Bulletin of the Seismological Society of America93, 2737–2751.
9.
CumminsD. J., 2008. CAT bonds and other risk-linked securities: State of the market and recent developments, Risk Management and Insurance Review11, 23–47.
10.
De ArmasJ., CalvetL., FrancoG., LopemanM., and JuanA. A., 2016. Minimizing trigger error in parametric earthquake catastrophe bonds via statistical approaches, in Modeling and Simulation in Engineering, Economics and Management (LeónR., Muñoz-TorresM. J., and MonevaJ. M., eds.), Springer, Cham, Switzerland, 167–175.
11.
EdesessM., 2015. Catastrophe bonds: An important new financial instrument, Alternative Investment Analyst Review4, 6–11.
12.
EdmondsC., 2012. Learning it the hard way: An analysis of cat bond investing pitfalls, in Alternative (Re)insurance Strategies, 2nd Ed. (LaneM., ed.), Risk Books, London, 269–294.
13.
ErdikM., SesetyanK., DemirciogluM., HancilarU., ZulfikarC., CaktiE., KamerY., YenidoganC., TuzunC., CagnanZ., and HarmandarE., 2010. Rapid earthquake hazard and loss assessment for Euro-Mediterranean region, Acta Geophysica58, 855–892.
14.
FrancoG., 2010. Minimization of trigger error in cat-in-a-box parametric earthquake catastrophe bonds with an application to Costa Rica, Earthquake Spectra26, 983–998.
15.
GodaK., 2013. Basis risk for earthquake catastrophe bond trigger using scenario-based versus station intensity-based approaches: A case study for Southwestern British Columbia, Earthquake Spectra29, 757–775.
16.
GurenkoE. (ed.), 2004. Catastrophe Risk and Reinsurance: A Country Risk Management Perspective, Risk Books, London, 322 pp.
17.
JayaramN., and BakerJ. W., 2009. Correlation model for spatially distributed ground-motion intensities, Earthquake Engineering and Structural Dynamics38, 1687–1708.
18.
KohrangiM., BazzuroP., VamvatsikosD., and SpillaturaA., 2017. Conditional spectrum-based ground motion record selection using average spectral acceleration, Earthquake Engineering and Structural Dynamics46, 1667–1685. (Corrections to this paper: Earthquake Engineering and Structural Dynamics 47, 265).
19.
LucoN., and CornellC. A., 2007. Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions, Earthquake Spectra23, 357–392.
20.
PaganiM., MonelliD, WeatherillG., DanciuL., CrowleyH., SilvaV., HenshawP., ButlerL., NastasiM., PanzeriL., SimionatoM., and ViganoD., 2014. OpenQuake Engine: An open hazard (and risk) software for the Global Earthquake Model, Seismological Research Letters85, 692–702.
21.
PuccianoS., FrancoG., and BazzurroP., 2017. Loss predictive power of strong motion networks for usage in parametric risk transfer: Istanbul as a case study, Earthquake Spectra33, 1513–1531.
22.
ReillyJ., DashtiS., ErvastiM., BrayJ. D., GlaserS. D., and BayenA. M., 2013. Mobile phones as seismologic sensors: Automating data extraction for the iShake system, IEEE Transactions on Automation Science and Engineering10, 242–251.
23.
SatoI., YashiroH., and FukushimaS., 2007. A study on design method of seismic risk derivatives focusing on the reduction in basis risk, Journal of Architecture and Planning (Transactions of AIJ)72, 261–268.
24.
SesetyanK., ZulfikarC., DemirciogluM., HancilarU., KamerY., and ErdikM., 2011. Istanbul Earthquake Rapid Response System: Methods and practices, Soil Dynamics and Earthquake Engineering31, 170–180.
WaldD., and FrancoG., 2016. Money matters: Rapid post-earthquake financial decision making, Natural Hazards Observer, Vol. XL, No. 7, 2 November 2016, Natural Hazards Center, University of Colorado, Boulder.
27.
WaldD. J., and FrancoG., 2017. Financial decision-making based on near-real-time earthquake information, Paper No. 3625, in Proceedings, 16th World Conference on Earthquake Engineering, 9–11 January, 2017, Santiago, Chile.
28.
WaldD. J., QuitorianoV., HeatonT. H., and KanamoriH., 1999. Relationships between peak ground acceleration, peak ground velocity, and modified mercalli intensity in California, Earthquake Spectra15, 557–564.
29.
WeatherillG. A., SilvaV., CrowleyH., and BazzurroP., 2015. Exploring the impact of spatial correlations and uncertainties for portfolio analysis in probabilistic seismic loss estimation, Bulletin of Earthquake Engineering13, 957–981.
30.
WoessnerJ., LaurentiuD., GiardiniD., CrowleyH., CottonF., GrünthalG., ValensiseG., ArvidssonR., BasiliR., DemirciogluM. B., HiemerS., MelettiC., MussonR. W., RovidaA. N., SesetyanK., and StucchiM., and the SHARE Consortium, 2015. The 2013 European Seismic Hazard Model: Key components and results, Bulletin of Earthquake Engineering13, 3553–3596.
