Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Mitigating geographical basis risk of weather derivatives using spatial-temporal regime-switching temperature model

1 Pan African University, Institute for Basic Sciences, Technology, and Innovation, Kenya
2 University of Nairobi, Kenya
3 African Institute for Mathematical Sceinces, South Africa

Topical Section: Mathematical modeling

In this paper, geographical basis risk in weather derivative design and pricing is mitigated by using spatial-temporal pricing models. A two-state regime-switching temperature model is constructed and extended to multi-dimensional locations that are highly correlated in temperature. The “normal” and “shifted” regime of this model are characterized by a heteroscedastic Ornstein-Uhlenbeck process and a Brownian motion with mean different from zero respectively. The correlation between the driving noise in each regime is assumed to be a function of the space between the locations and increases with decreasing space. A weight is assigned to each location in the temperature basket. However, a location with a higher risk is assigned a larger weight and vice versa. The weightings in the temperature basket gave considerable importance to farming locations having greater exposure to temperature risk. The further the farming location from the weather station, the larger the weight. With this spatial-temporal weather derivatives pricing model, the holder of a weather derivative contract will have the opportunity to select the most appropriate composite of weather stations with their desired weight that can reduce geographical basis risks.
  Figure/Table
  Supplementary
  Article Metrics

References

1. UNECA, Challenges to agricultural development in africa. In economic report on africa 2009 developing african agriculture through regional value chains, 2009.

2. M. Parry, M. L. Parry, O. Canziani, et al. Climate change 2007-impacts, adaptation and vulnerability: Working group II contribution to the fourth assessment report of the IPCC, Cambridge University Press, 2007

3. W. L. Filho, A. O. Esilaba, K. P. Rao, et al. Adapting African Agriculture to Climate Change, Springer, 2015.

4. O. Musshoff, M. Odening, W. Xu, Management of climate risks in agriculture-will weather derivatives permeate? Applied economics, 43 (2011), 1067-1077.

5. A. Stoppa, U. Hess, Design and use of weather derivatives in agricultural policies: the case of rainfall index insurance in morocco. In: International Conference "Agricultural Policy Reform and the WTO: Where are we heading", Capri (Italy), Citeseer.

6. P. Alaton, B. Djehiche, D. Stillberger, On modelling and pricing weather derivatives, Applied Mathematical Finance, 9 (2002), 1-20.    

7. R. Elias, M.Wahab, L. Fang, A comparison of regime-switching temperature modeling approaches for applications in weather derivatives, Eur. J. Oper. Res., 232 (2014), 549-560.    

8. M. Mraoua, Temperature stochastic modeling and weather derivatives pricing: empirical study with moroccan data, Afrika Statistika, 2 (2009).

9. F. E. Benth, J. Šaltytė-Benth, Stochastic modelling of temperature variations with a view towards weather derivatives, Applied Mathematical Finance, 12 (2005), 53-85.    

10. S. A. Gyamerah, P. Ngare, D. Ikpe, Hedging crop yields against weather uncertainties-a weather derivative perspective, arXiv preprint arXiv:1905.07546, 2019.

11. J. š. Benth, F. E. Benth, P. Jalinskas, A spatial-temporal model for temperature with seasonal variance, J. Appl. Stat., 34 (2007), 823-841.    

12. C. De Jong, R. Huisman, Option formulas for mean-reverting power prices with spikes, 2002.

13. Wikipedia, Growing degree-day, 2018. Available from: https://en.wikipedia.org/wiki/Growing_degree-day.

14. S. E. Shreve, Stochastic calculus for finance II: Continuous-time models, Springer Science & Business Media, 2004.

15. R. S. Dischel, Climate risk and the weather market: financial risk management with weather hedges, Risk Books London, 2002.

16. R. McIntyre, S. Doherty, Weather risk-an example from the uk, Energy and Power Risk Management June, 1999.

17. E. Evarest, F. Berntsson, M. Singull, et al. Regime switching models on temperature dynamics, 2016.

18. S. A. Gyamerah, P. Ngare, D. Ikpe, Regime-switching temperature dynamics model for weather derivatives, International Journal of Stochastic Analysis, 2018 (2018).

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved