Startseite Wirtschaftswissenschaften Hurricane Bond Price Dependency on Underlying Hurricane Parameters
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Hurricane Bond Price Dependency on Underlying Hurricane Parameters

  • Carolyn W. Chang ORCID logo EMAIL logo und Yalan Feng ORCID logo
Veröffentlicht/Copyright: 31. August 2020

Abstract

Hurricane bonds are parametric in nature as they have a dual-exercise structure: the first exercise is conditional on the hurricane’s physical landfall location and the second is conditional upon the embedded option ending in-the-money. We propose a coupled and physically-based hurricane bond pricing model via Monte Carlo simulation that resolves the dual exercise, which was not addressed in extant loss-based catastrophe bond pricing models. This coupled model is developed at the nexus of atmospheric science and finance by integrating hurricane risk modeling and option pricing. By applying this model to price a parametric hurricane bond, we demonstrate how a hurricane bond’s price is sensitive to its underlying hurricane’s physical parameters – genesis, heading, translation speed, velocity, and radius.

JEL classification: G13; G17; G22

Corresponding author: Carolyn W. Chang, Department of Finance, California State University, Fullerton, USA, E-mail:

References

Artemis. Cat Bonds in Holding Pattern; Florida on Watch for Hurricane Matthew. (Published on October 4, 2016). http://www.artemis.bm/.Suche in Google Scholar

Bowers, N. L., J. H. U. Gerber, J. C. Hickman, D. A. Jones, and C. J. Nesbitt. 1986. Actuarial Mathematics. Itasca, IL: Society of Actuaries.Suche in Google Scholar

Burnecki, K., M. N. Giuricich, and Z. Palmowski. 2019. “Valuation of Contingent Convertible Catastrophe Bonds – The Case for Equity Conversion.” Insurance: Mathematics and Economics 88: 238–54, https://doi.org/10.1016/j.insmatheco.2019.07.006.Suche in Google Scholar

Chang, C., J. S. K. Chang, and M. Wen. 2014. “Optimum Hurricane Futures Hedge in a Warming Environment: a Risk-Return Jump-Diffusion Approach.” Journal of Risk and Insurance 81 (1): 199–217, https://doi.org/10.1111/j.1539-6975.2012.01492.x.Suche in Google Scholar

Chang, C. C., J. W. Yang, and M. T. Yu. 2018. “Hurricane Risk Management with Climate and CO2 Indices.” Journal of Risk and Insurance 85 (3): 695–720, https://doi.org/10.1111/jori.12182.Suche in Google Scholar

Chang, C., J. S. K. Chang, M. T. Yu, and Y. Zhao. 2020. Portfolio Optimization in the Catastrophe Space. European Financial Management. forthcoming.10.1111/eufm.12265Suche in Google Scholar

Emanuel, K., S. Ravela, E. Vivant, and C. Risi. 2006. “A Statistical Deterministic Approach to Hurricane Risk Assessment.” Bulletin of the American Meteorological Society 87 (3): 299–314, https://doi.org/10.1175/bams-87-3-299.Suche in Google Scholar

Emanuel, K., R. Sundararajan, and J. Williams. 2008. “Hurricanes and Global Warming: Results from Downscaling IPCC AR4 Simulations.” Bulletin of the American Meteorological Society 89: 347–68, https://doi.org/10.1175/bams-89-3-347.Suche in Google Scholar

Emanuel, K. 2006. “Climate and Tropical Cyclone Activity: A New Model Downscaling Approach.” Journal of Climate 19: 4797–802, https://doi.org/10.1175/jcli3908.1.Suche in Google Scholar

Emanuel, K. 2017. “A Fast Intensity Simulator for Tropical Cyclone Risk Analysis.” Natural Hazards: 88: 779–96. https://doi.org/10.1007/s11069-017-2890-7.Suche in Google Scholar

Emanuel, K., C. DesAutels, C. Holloway, R. Korty. 2004. “Environmental Control of Tropical Cyclone Intensity.” Journal of the Atmospheric Sciences 61: 843–58, https://doi.org/10.1175/1520-0469(2004)061%3C0843:ECOTCI%3E2.0.CO;2.10.1175/1520-0469(2004)061<0843:ECOTCI>2.0.CO;2Suche in Google Scholar

Gatzert, N., S. Pokutta, and N. Vogl. 2019. “Convergence of Capital and Insurance Markets: Consistent Pricing of Index-linked Catastrophe Loss Instruments.” Journal of Risk and Insurance 86 (1): 39–72, https://doi.org/10.1111/jori.12191.Suche in Google Scholar

Gürtler, M., M. Hibbeln, and C. Winkelvos. 2016. “The Impact of the Financial Crisis and Natural Catastrophes on CAT bonds.” Journal of Risk and Insurance 83 (3): 579–612, https://doi.org/10.1111/jori.12057.Suche in Google Scholar

Härdle, W. K., and B. Lopez-Cabrera. 2010. “Calibrating CAT bonds for Mexican Earthquakes.” Journal of Risk & Insurance 77: 625–50, https://doi.org/10.1111/j.1539-6975.2010.01355.x.Suche in Google Scholar

Hoyt, R. E., and K. A. McCullough. 1999. “Catastrophe Insurance Options: Are They Zero-Beta Assets?.” Journal of Insurance Issues 22 (2): 147–63. https://www.jstor.org/stable/41946178.Suche in Google Scholar

Jarrow, R. A. 2010. “A Simple Robust Model for Cat Bond Valuation.” Finance Research Letters 7 (2): 72–9, https://doi.org/10.1016/j.frl.2010.02.005.Suche in Google Scholar

Lin, S. K., C. C. Chang, and M. T. Yu. 2011. “Valuation of Catastrophe Equity Puts with Markov-Modulated Poisson Processes.” Journal of Risk and Insurance 78 (2): 447–73. https://doi.org/10.1111/j.1539-6975.2010.01385.x.Suche in Google Scholar

Lin, J., K. Emanuel, and J. L. Vigh. 2019. Forecasts of Hurricanes using Large-Ensemble Outputs. Boulder, Colorado: MIT and National Center for Atmospheric Research. Working Paper.10.1175/WAF-D-19-0255.1Suche in Google Scholar

Lo, C. L., C. Chang, J. P. Lee, and M. T. Yu. 2013. “Valuation of Insurers’ Contingent Capital with Counterparty Risk and Price Endogeneity.” Journal of Banking and Finance 37 (12): 5025–35, https://doi.org/10.1016/j.jbankfin.2013.09.007.Suche in Google Scholar

Lo, C. L., C. Chang, J. P. Lee, and M. T. Yu. 2020. “Pricing Catastrophe Swaps with Default Risk and Stochastic Interest Rates.” Pacific-Basin Finance Journal 58. https://doi.org/10.1016/j.pacfin.2020.101314.Suche in Google Scholar

Loubergé, H., E. Kellezi, and M. Gilli. 1999. “Using Catastrophe-Linked Securities to Diversify Insurance Risk: A Financial Analysis of Cat Bonds.” Journal of Insurance Issues 22 (2): 125–46. https://www.jstor.org/stable/41946177.Suche in Google Scholar

Marks, D. G. 1992. The Beta and Advection Model for Hurricane Track Forecasting. US: US Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, National Meteorological Center.Suche in Google Scholar

Vickery, P. J., P. F. Skerlj, and L. A. Twisdale. 2000. “Simulation of Hurricane Risk in the US using Empirical Track Model.” Journal of Structural Engineering 126 (10): 1222–37, https://doi.org/10.1061/(asce)0733-9445(2000)126:10(1222).10.1061/(ASCE)0733-9445(2000)126:10(1222)Suche in Google Scholar

Yonekura, E., and T. M. Hall. 2011. “A Statistical Model of Tropical Cyclone Tracks in the Western North Pacific with ENSO-dependent Cyclogenesis.” Journal of Applied Meteorology and Climatology 50: 1725–39, https://doi.org/10.1175/2011jamc2617.1.Suche in Google Scholar

Zhao, Y., and M. Yu. 2019. “Measuring the Liquidity Impact on Catastrophe Bond Spreads.” Pacific-Basin Finance Journal 56: 197–210, https://doi.org/10.1016/j.pacfin.2019.06.006.Suche in Google Scholar

Received: 2020-05-01
Accepted: 2020-06-15
Published Online: 2020-08-31

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 10.3.2026 von https://www.degruyterbrill.com/document/doi/10.1515/apjri-2020-0017/html
Button zum nach oben scrollen