管理评论 ›› 2022, Vol. 34 ›› Issue (5): 3-12.

• 经济与金融管理 •    下一篇

天气衍生品空间基差风险对冲方法实证比较研究

李永, 石凤, 姜志堂   

  1. 同济大学经济与管理学院, 上海 200092
  • 收稿日期:2019-03-25 出版日期:2022-05-28 发布日期:2022-06-17
  • 通讯作者: 姜志堂(通讯作者),同济大学经济与管理学院硕士研究生。
  • 作者简介:李永,同济大学经济与管理学院副教授,博士生导师,博士;石凤,同济大学经济与管理学院硕士研究生
  • 基金资助:
    国家社会科学基金一般项目(15BJY127);国家社会科学基金重大项目(16ADA052)。

An Empirical and Comparative Study into the Approaches of Hedging the Geographical Basis Risk of Weather Derivatives

Li Yong, Shi Feng, Jiang Zhitang   

  1. School of Economics and Management, Tongji University, Shanghai 200092
  • Received:2019-03-25 Online:2022-05-28 Published:2022-06-17

摘要: 空间基差风险削弱了天气衍生品对天气风险的规避效果,需要结合数据特征对不同的对冲方法效果比较选择。以气温期权空间基差风险对冲理论为依据,选取山东潍坊、江苏南京1978—2015年日平均气温数据,分别选取线性组合、反距离加权方法构建气温期权空间组合,比较检验了空间基差风险的对冲效果。研究发现,气温期权空间组合数量在一定范围内时,线性组合法和反距离加权法下RMSE均呈现出“U”型变化趋势,存在最优购买组合;两种方法均可降低天气衍生品的空间基差风险,然而反距离加权法下RMSE变化的波动较小,并随着幂指数增大对冲效果波动减小,更易于确定购买组合数量。

关键词: 空间基差风险, 气温期权, 反距离加权法, 天气风险

Abstract: The existence of geographical basis risk makes it less effective for weather derivatives to guard against weather risks. It is necessary to combine the characteristics of the data to compare the effects of different hedging approaches. In this paper, based on the hedging theory of geographical basis risk for temperature index options, we select the 1978-2015 daily average temperature data of Weifang, Shandong province and Nanjing, Jiangsu province in China as samples to construct a spatial portfolio of temperature index options by comparing the liner combination method with the inverse distance weighting method. The results demonstrate that, as the spatial-portfolio number of temperature index options varies in a certain range, the root mean square error (RMSE) values obtained by both liner combination method and inverse distance weighting method display a U-shapted change trend, which denotes the optimal portfolio exists. In addition, both the two methods can reduce the geographical basis risk of weather derivatives. In spite of that, the inverse distance weighting method causes slighter fluctuation of RMSE values, which decline as the power exponent increases. Thus, the inverse distance weighting method is more practical and is recommended for determining optimal portfolio weights of temperature index options.

Key words: geographical basis risk, temperature index options, inverse distance weighting, weather risk