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An Actuarial Pricing Method for Air Quality Index Options

Author

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  • Zhuoxin Liu

    (School of Economics and Management, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Laijun Zhao

    (China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai 200030, China
    Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Chenchen Wang

    (School of Economics and Management, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Yong Yang

    (School of Arts and Sciences, Shanxi University of Science & Technology, Xi’an 710021, China)

  • Jian Xue

    (School of Economics and Management, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Xin Bo

    (Appraisal Center for Environment and Engineering, Ministry of Environmental Protection, Beijing 100012, China)

  • Deqiang Li

    (School of Economics and Management, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Dengguo Liu

    (School of Automotive Studies, Tongji University, Shanghai 201804, China
    Shanghai Environment Monitoring Center, Shanghai 200235, China)

Abstract

Poor air quality has a negative impact on social life and economic production activities. Using financial derivatives to hedge risks is one of the important methods. Air quality index (AQI) options are designed to help enterprises cope with the operational risk caused by air pollution. First, the expanded Ornstein–Uhlenbeck model is established using an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) method to predict AQI for a city. Next, the average AQI is constructed to be as the underlying index for the AQI options. We then priced AQI options using an actuarial method with an Esscher transform. Meanwhile payoff functions for the options are established to let enterprises hedge against the operational risk caused by air pollution. Finally, we determined the price of AQI options using data from Xi’an, China, and the example of a tourism enterprise as a case study of how AQI options can be applied to hedge against operational risk for enterprises. With AQI options trading, enterprises can hedge against operational risks caused by air pollution. The applicability of AQI options is wide, it can also be applied in other cities or regions.

Suggested Citation

  • Zhuoxin Liu & Laijun Zhao & Chenchen Wang & Yong Yang & Jian Xue & Xin Bo & Deqiang Li & Dengguo Liu, 2019. "An Actuarial Pricing Method for Air Quality Index Options," IJERPH, MDPI, vol. 16(24), pages 1-19, December.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:24:p:4882-:d:293948
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    References listed on IDEAS

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    1. Benedikte Bjerge & Neda Trifkovic, 2018. "Extreme weather and demand for index insurance in rural India," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(3), pages 397-431.
    2. Jewson,Stephen & Brix,Anders, 2005. "Weather Derivative Valuation," Cambridge Books, Cambridge University Press, number 9780521843713, September.
    3. Wenhan Li & Lixia Liu & Guiwen Lv & Cuixiang Li, 2018. "Exchange option pricing in jump-diffusion models based on esscher transform," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(19), pages 4661-4672, October.
    4. Jian Liu & Lizhao Yan & Chaoqun Ma, 2013. "Pricing Options and Convertible Bonds Based on an Actuarial Approach," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, December.
    5. Gülpınar, Nalân & Çanakoḡlu, Ethem, 2017. "Robust portfolio selection problem under temperature uncertainty," European Journal of Operational Research, Elsevier, vol. 256(2), pages 500-523.
    6. Wolfgang Karl Hardle and Maria Osipenko, 2012. "Spatial Risk Premium on Weather Derivatives and Hedging Weather Exposure in Electricity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    7. Panle Jia Barwick & Shanjun Li & Deyu Rao & Nahim Bin Zahur, 2018. "The Morbidity Cost of Air Pollution: Evidence from Consumer Spending in China," Working Papers id:12825, eSocialSciences.
    8. Peter Alaton & Boualem Djehiche & David Stillberger, 2002. "On modelling and pricing weather derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 1-20.
    9. R. J. Erhardt, 2015. "Mid‐twenty‐first‐century projected trends in North American heating and cooling degree days," Environmetrics, John Wiley & Sons, Ltd., vol. 26(2), pages 133-144, March.
    10. Hainaut, Donatien, 2019. "Hedging of crop harvest with derivatives on temperature," LIDAM Reprints ISBA 2019003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Bladt, Mogens & Rydberg, Tina Hviid, 1998. "An actuarial approach to option pricing under the physical measure and without market assumptions," Insurance: Mathematics and Economics, Elsevier, vol. 22(1), pages 65-73, May.
    12. Fred ESPEN Benth & Jurate saltyte Benth, 2007. "The volatility of temperature and pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 7(5), pages 553-561.
    13. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    14. Jaspersen, Johannes G. & Richter, Andreas, 2015. "The wealth effects of premium subsidies on moral hazard in insurance markets," European Economic Review, Elsevier, vol. 77(C), pages 139-153.
    15. Panle Jia Barwick & Shanjun Li & Deyu Rao & Nahim Bin Zahur, 2018. "The Healthcare Cost of Air Pollution: Evidence from the World’s Largest Payment Network," NBER Working Papers 24688, National Bureau of Economic Research, Inc.
    16. Hainaut, Donatien, 2019. "Hedging of crop harvest with derivatives on temperature," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 98-114.
    17. Hong Shi & Zhihui Jiang, 2016. "The efficiency of composite weather index insurance in hedging rice yield risk: evidence from China," Agricultural Economics, International Association of Agricultural Economists, vol. 47(3), pages 319-328, May.
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