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Forecasting of daily dynamic hedge ratio in agricultural and commodities’ futures markets: evidence from Garch models

机译:预测农产品和商品期货市场的日动态套期保值比率:来自Garch模型的证据

摘要

This thesis investigates the predictive power of six bivariate GARCH-CCC (constant conditional correlation) models; the GARCH (1, 1), BEKK GARCH (1, 1), GARCH-X (1, 1), BEKK-X (1, 1), GARCH-GJR (1, 1) and QGARCH (1, 1) based on both normal and student’s t distributions. Empirical investigations are conducted by forecasting the daily hedge ratios from agricultural futures markets using one-step-ahead over 1 year and 2 year out-of-sample period. The forecasting of OHR in agricultural and commodities’ futures markets has not been studied thoroughly and few publications are available in literature. My work enriches the literature and will hopefully provide guidance for hedging in these markets.To forecast the OHR, we apply data from three storable commodities, coffee, wheat and soybean and two non-storable commodities, live cattle and live hog. Four tests are conducted to evaluate the forecasting errors of out-of-sample forecasted return of the portfolio based on the forecasted OHR.Our study shows that the asymmetric GARCH model outperforms other models, and the standard GARCH is the weakest for 1-year forecast. However, the standard GARCH model performs well for 2-year forecast of live cattle with student’s t distributed residuals. More generally, the BEKK and asymmetric GJR and QGARCH models are recommended to forecast OHR on both 1-year and 2-year horizons with normal and student’s t distributions for storable products and the asymmetric models for non-storable commodities. Furthermore, our study demonstrates that the predictive power of GARCH models depends on the distribution of residuals, the commodity and also the length of the forecast horizons. This result is consistent with the those from Poon and Granger (2003) and Chen et.al (2003). Given accurately forecasted OHR, investors can determine appropriate hedging strategies for portfolio management to reduce or transfer risks, and prepare for the capital needed for hedging.
机译:本文研究了六个双变量GARCH-CCC(恒定条件相关)模型的预测能力;基于GARCH(1、1),BEKK GARCH(1、1),GARCH-X(1、1),BEKK-X(1、1),GARCH-GJR(1、1)和QGARCH(1、1)在正态分布和学生t分布上。通过使用超过1年和超过2年的非采样期提前一步来预测农业期货市场的每日对冲比率来进行实证研究。对农产品和大宗商品期货市场中的OHR的预测尚未进行深入研究,文献中很少有出版物。我的工作丰富了文献资料,并有望为在这些市场中进行套期保值提供指导。为了预测OHR,我们应用了来自三种可储存商品(咖啡,小麦和大豆)和两种不可储存商品(活牛和生猪)的数据。基于预测的OHR进行了四项测试,以评估投资组合的样本外预测收益的预测误差。我们的研究表明,非对称GARCH模型优于其他模型,而标准GARCH在1年预测中最弱。但是,标准GARCH模型对于带有学生t分布残差的活牛的2年预测效果很好。更一般而言,建议使用BEKK模型和非对称GJR模型和QGARCH模型来预测1年和2年期的OHR,以及可存储产品的正态分布和学生t分布,以及不可存储商品的非对称模型。此外,我们的研究表明,GARCH模型的预测能力取决于残差,商品的分布以及预测范围的长度。这一结果与Poon和Granger(2003)以及Chen等人(2003)的结果一致。有了准确预测的OHR,投资者可以为投资组合管理确定适当的对冲策略,以减少或转移风险,并为对冲所需的资金做准备。

著录项

  • 作者

    Zhang Yuanyuan;

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  • 年度 2012
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  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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