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Self-exciting Threshold Auto-regressive Model For Evaluating Hedging Cost

机译:评估对冲成本的自我激励阈值自动回归模型

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In principle of no-arbitrage,price movements should best be described by a first order vector error correction model,with the error correction term being the price differential between spot and futures markets (the basis).Evidence from Chinese markets suggests that basis series follow a multi-order auto regressive model,which means that there are persistent arbitrage opportunities than should be in functioning markets.With the data base of Chinese copper futures market,using self-exciting threshold autoregressive (SETAR) model,we analyze whether such dynamics can be related to variable arbitrage cost.Our findings reveal that arbitrage opportunities are decreasing with the development and effectiveness of Chinese markets.Furthermore,a simple and general scheme is presented for establishing SETAR model to evaluate hedging cost.With the improved algorithm,both of threshold values and autoregressive coefficients may be optimized.The empirical research shows the scheme was practical and efficient.
机译:原则上,禁止套利,最佳价格变动应该通过一阶向量纠错模型来描述,纠错术语是现货和期货市场之间的价格差异(基础)。来自中国市场的差异,从而提出基础系列多阶自动回归模型,这意味着存在持久的套利机会,而不是在运作市场中的运作市场。在中国铜期货市场的数据库,采用自我兴奋的阈值自动增加(SETAR)模型,我们分析了这种动态是否可以与可变套利成本有关。我们调​​查结果表明,随着中国市场的发展和有效性,套利机会正在减少。提出了一种简单且一般的方案,用于建立租赁模型来评估对冲成本。随着阈值的改进算法可以优化值和自回归系数。实证研究表明该方案实用性和效果icient。

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