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Efficiency of weather derivatives for Chinese agriculture industry

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  • Manuela Ender
  • Ruyuan Zhang

Abstract

Purpose - – The purpose of this paper is to analyze the efficiency of temperature-based weather derivatives (WD) in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions. Design/methodology/approach - – The objective of this paper is to analyze the efficiency of temperature-based WD in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions. Findings - – The results of the efficiency tests show that temperature-based put options are efficient in offsetting yield shortfalls for rice and wheat in China. The weather-yield models have a high prediction power in explaining yield variation by temperature. Research limitations/implications - – The de-trending procedure for the weather-yield model should be improved to distinguish better between technology progress, human activities and influence of weather. Further, more advanced models could be used for the pricing. Practical implications - – The findings of the paper support the launch of WD as an efficient risk management tool for agriculture in China. Compared with traditional damage-based insurance, WD are more flexible, have lower transactions costs and avoid moral hazard or adverse selection. Originality/value - – The efficiency problem of WD has not been analyzed sufficiently worldwide and especially not for developing countries like China where a large proportion of the population works as farmers. This paper supports to fill this gap.

Suggested Citation

  • Manuela Ender & Ruyuan Zhang, 2015. "Efficiency of weather derivatives for Chinese agriculture industry," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 7(1), pages 102-121, February.
  • Handle: RePEc:eme:caerpp:v:7:y:2015:i:1:p:102-121
    DOI: 10.1108/CAER-06-2013-0089
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    Citations

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    Cited by:

    1. Lu Zong & Manuela Ender, 2016. "Spatially-Aggregated Temperature Derivatives: Agricultural Risk Management in China," IJFS, MDPI, vol. 4(3), pages 1-17, September.
    2. Gregory, Richard P., 2024. "Risk premiums from temperature trends," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 505-525.
    3. Lu Zong & Manuela Ender, 2018. "Comparison of Stochastic and Spline Models for Temperature‐based Derivatives in China," Pacific Economic Review, Wiley Blackwell, vol. 23(4), pages 547-589, October.
    4. Martina Bobriková, 2016. "Weather Risk Management in Agriculture," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 64(4), pages 1303-1309.

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