IDEAS home Printed from https://ideas.repec.org/p/hit/cisdps/643.html
   My bibliography  Save this paper

Fiscal Reform and Improved Earthquake Insurance Claims-paying Capacity: Can the Two Coexist? —Attempting to reconcile heightened earthquake risk with sound fiscal policy—

Author

Listed:
  • Oguro, Kazumasa
  • Hiraizumi, Nobuyuki
  • Owen, Michael
  • Guo, Jicang

Abstract

From the standpoint of reconciling heightened earthquake risk with sound fiscal policy, this paper performs a simplified simulation analysis of obtainable risk reduction in proportion to reinsurance premiums to explore the potential for improving the claims-paying capacity of Japan’s earthquake insurance program by using reinsurance, which is currently considered the least expensive method for improving risk transfer/claims-paying capacity. We divided the roughly 5 trillion yen of risk that is currently retained by Japan’s earthquake insurance program into 21 layers, starting with four successive layers in the 200 billion yen to 1 trillion yen group and ending with four successive layers in the 4.2 to 5 trillion yen group. We then compared the price of risk (reinsurance premiums necessary for reducing one unit of risk) for the different layers. Our analysis indicated that the four layers in the 1.4 to 2.2 trillion yen group could be reinsured for the lowest price per unit risk. Hence, if these successive four layers were ceded, the reinsurance premiums to be paid under the base insurance premiums to be paid under the base case would be 42.5 billion yen (a 5.31% reinsurance premium rate is applied for ceding 800 billion yen risk), thereby making possible risk reduction on the order of 698.5 billion (99% Tail VaR).

Suggested Citation

  • Oguro, Kazumasa & Hiraizumi, Nobuyuki & Owen, Michael & Guo, Jicang, 2015. "Fiscal Reform and Improved Earthquake Insurance Claims-paying Capacity: Can the Two Coexist? —Attempting to reconcile heightened earthquake risk with sound fiscal policy—," CIS Discussion paper series 643, Center for Intergenerational Studies, Institute of Economic Research, Hitotsubashi University.
    • Handle: RePEc:hit:cisdps:643
    as

    Download full text from publisher

    File URL: https://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/27203/cis_dp643.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gijbels, Irène & Sznajder, Dominik, 2013. "Testing tail monotonicity by constrained copula estimation," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 338-351.
    2. S. A. Abu Bakar & Saralees Nadarajah & Z. A. Absl Kamarul Adzhar, 2018. "Loss modeling using Burr mixtures," Empirical Economics, Springer, vol. 54(4), pages 1503-1516, June.
    3. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    4. Gencay, Ramazan & Selcuk, Faruk & Ulugulyagci, Abdurrahman, 2003. "High volatility, thick tails and extreme value theory in value-at-risk estimation," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 337-356, October.
    5. Goran Andjelic & Ivana Milosev & Vladimir Djakovic, 2010. "Extreme Value Theory In Emerging Markets," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 55(185), pages 63-106, April - J.
    6. Kittiya Chaithep & Songsak Sriboonchitta & Chukiat Chaiboonsri & Pathairat Pastpipatkul, 2012. "Value at Risk Analysis of Gold Price Returns Using Extreme Value Theory," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(4), pages 151-168, December.
    7. Zhi-Fu Mi & Yi-Ming Wei & Bao-Jun Tang & Rong-Gang Cong & Hao Yu & Hong Cao & Dabo Guan, 2017. "Risk assessment of oil price from static and dynamic modelling approaches," Applied Economics, Taylor & Francis Journals, vol. 49(9), pages 929-939, February.
    8. Madhusudan Karmakar, 2013. "Estimation of tail‐related risk measures in the Indian stock market: An extreme value approach," Review of Financial Economics, John Wiley & Sons, vol. 22(3), pages 79-85, September.
    9. Miljkovic, Tatjana & Grün, Bettina, 2016. "Modeling loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 387-396.
    10. Weshah Razzak, "undated". "On the GCC Currency Union," API-Working Paper Series 0910, Arab Planning Institute - Kuwait, Information Center.
    11. Athanasios Sachlas & Takis Papaioannou, 2014. "Residual and Past Entropy in Actuarial Science and Survival Models," Methodology and Computing in Applied Probability, Springer, vol. 16(1), pages 79-99, March.
    12. Dr. Ibrahim Onour, "undated". "The Global Financial Crisis and Equity Markets in Middle East Oil Exporting Countries," API-Working Paper Series 1009, Arab Planning Institute - Kuwait, Information Center.
    13. Reynkens, Tom & Verbelen, Roel & Beirlant, Jan & Antonio, Katrien, 2017. "Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 65-77.
    14. Queensley C. Chukwudum, 2019. "Reinsurance Pricing of Large Motor Insurance Claims in Nigeria: An Extreme Value Analysis," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 8(4), pages 1-12, July.
    15. Cristiano Villa, 2017. "Bayesian estimation of the threshold of a generalised pareto distribution for heavy-tailed observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 95-118, March.
    16. Arthur Charpentier & Emmanuel Flachaire, 2021. "Pareto Models for Risk Management," Dynamic Modeling and Econometrics in Economics and Finance, in: Gilles Dufrénot & Takashi Matsuki (ed.), Recent Econometric Techniques for Macroeconomic and Financial Data, pages 355-387, Springer.
    17. Bernardi, Mauro & Maruotti, Antonello & Petrella, Lea, 2012. "Skew mixture models for loss distributions: A Bayesian approach," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 617-623.
    18. Dey Ashim Kumar & Das Kumer Pial, 2020. "Predicting Federal Funds Rate Using Extreme Value Theory," Stochastics and Quality Control, De Gruyter, vol. 35(1), pages 1-15, June.
    19. Sarra Ghaddab & Manel Kacem & Christian Peretti & Lotfi Belkacem, 2023. "Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty," Empirical Economics, Springer, vol. 65(3), pages 1105-1127, September.
    20. M. Ivette Gomes & Armelle Guillou, 2015. "Extreme Value Theory and Statistics of Univariate Extremes: A Review," International Statistical Review, International Statistical Institute, vol. 83(2), pages 263-292, August.

    More about this item

    Keywords

    Government Special Account reform; earthquake insurance program; claims-paying capacity; reinsurance; price of risk; Tail VaR;
    All these keywords.

    JEL classification:

    • H60 - Public Economics - - National Budget, Deficit, and Debt - - - General
    • H61 - Public Economics - - National Budget, Deficit, and Debt - - - Budget; Budget Systems
    • H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hit:cisdps:643. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Digital Resources Section, Hitotsubashi University Library (email available below). General contact details of provider: https://edirc.repec.org/data/cihitjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.