A Dynamic Programming Approach for Pricing Weather Derivatives under Issuer Default Risk
Wolfgang Härdle () and
Maria Osipenko ()
Additional contact information
Maria Osipenko: Ladislaus von Bortkiewicz Chair of Statistics, School of Business and Economics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
International Journal of Financial Studies, 2017, vol. 5, issue 4, 1-18
Weather derivatives are contingent claims with payoff based on a pre-specified weather index. Firms exposed to weather risk can transfer it to financial markets via weather derivatives. We develop a utility-based model for pricing baskets of weather derivatives under default risk on the issuer side in over-the-counter markets. In our model, agents maximise the expected utility of their terminal wealth, while they dynamically rebalance their weather portfolios over a finite investment horizon. Using dynamic programming approach, we obtain semi-closed forms for the equilibrium prices of weather derivatives and for the optimal strategies of the agents. We give an example on how to price rainfall derivatives on selected stations in China in the universe of a financial investor and a weather exposed crop insurer.
Keywords: dynamic programming; pricing; risk management (search for similar items in EconPapers)
JEL-codes: G1 G2 G3 F2 F3 F41 F42 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gam:jijfss:v:5:y:2017:i:4:p:23-:d:115840
Access Statistics for this article
International Journal of Financial Studies is currently edited by Prof. Dr. Nicholas Apergis
More articles in International Journal of Financial Studies from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().