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印度大宗商品的随机价差配对交易

文章 arXiv papers · 作者: Dhruv Mahajan et al.
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总结

本研究使用 17 种印度大宗商品的现货价格测试配对策略，涵盖能源、金属和农业，时间范围为 2010 至 2018 年。研究在训练样本上使用 Johansen 协整检验筛选具有长期关联的商品配对，然后用单因子随机方法对各入选配对的对数价差进行建模。

模型参数通过差分进化估计，交易规则参数则通过训练期回测调优，并沿用至后续测试期。研究从 136 个候选配对中选出十二组，报告称所有入选配对在测试期的夏普比率均超过 1.4。研究结果受一项假设限制，即训练期的协整关系在测试期仍然持续；本文没有说明交易成本或其他实施摩擦。

核心观点

  • Johansen 协整检验用于从大宗商品现货价格中识别候选配对。
  • 该策略通过协整配对的对数价差进行建模。
  • 随机模型参数通过差分进化估计。
  • 交易规则在训练数据上优化，并应用于后续测试期。
  • 据报告，所有入选配对的测试期夏普比率均高于 1.4。

标签

大宗商品配对交易均值回归回测统计学
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# Stochastic Spread Pairs Trading in the Indian Commodity Market


# Stochastic Spread Pairs Trading in the Indian Commodity Market









In this study, we applied a stochastic spread pairs trading strategy on the Indian commodity market. The complete set of commodities were taken whose spot price was available for the period of January 1st 2010 to December 31st 2018 including energy, metals and the agricultural commodity sector. Spot data was taken from the MCX pooled spot prices for 17 commodities. The data was split into training period (January 1st 2010 to 14th March 2017) and testing period(15th Match 2017 to 31st December 2018). The splitting was done using a 80:20 split.Johanssen Cointegration tests were done on training data for pairs of commodities to check for long-run relationship and the cointegrated commodities were selected for formation of the trading process. We found a total of 12 cointegrated pairs out of 136 possible pairs. Cointegration was assumed for the testing period. A single-factor stochastic trading approach was applied on the logarithmic spread of the cointegrated pairs for both the training and testing period.The parameters of stochastic spread model were estimated using differential evolution algorithm. Also parameters for the trading rule were optimized by backtesting on the training period and assumed for the testing period. The results show a sharpe ratio of above 1.4 for all the commodity cointegrated pairs in the backtesing period.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。