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英文文献：Economic significance of commodity return forecasts from the fractionally cointegrated VAR model-从小协整VAR模型预测商品收益的经济意义
英文文献作者：Sepideh Dolatabadi,Paresh Kumar Narayan,Morten ?rregaard Nielsen,Ke Xu
英文文献摘要：
Based on recent evidence of fractional cointegration in commodity spot and futures markets, we investigate whether a fractionally cointegrated model can provide statistically and/or economically significant forecasts of commodity returns. Specifically, we propose to model and forecast commodity spot and futures prices using a fractionally cointegrated vector autoregressive (FCVAR) model that generalizes the more well-known (non-fractional) CVAR model to allow fractional integration. We derive the best linear predictor for the FCVAR model and perform an out-of-sample forecast comparison with the non-fractional model. In our empirical analysis to daily data on 17 commodity markets, the fractional model is found to be superior in terms of in-sample fit and also out-of-sample forecasting based on statistical metrics of forecast comparison. We analyze the economic significance of the forecasts through a dynamic trading strategy based on a portfolio with weights derived from a mean-variance utility function. Although there is much heterogeneity across commodity markets, this analysis leads to statistically significant and economically meaningful profits in most markets, and shows that profits from both the fractional and non-fractional models are higher on average and statistically more significant than profits derived from a simple moving-average strategy. The analysis also shows that, in spite of the statistical advantage of the fractional model, the fractional and non-fractional models generate very similar profits with only a slight advantage to the fractional model on average.
基于最近商品现货和期货市场中分数协整的证据，我们研究了一个分数协整模型是否能够提供统计和/或经济上对商品收益的重要预测。具体地说，我们建议使用一种极小协整向量自回归(FCVAR)模型来建模和预测商品现货和期货价格，该模型推广了更著名的(非分数式)CVAR模型，从而允许分数式积分。我们得到了FCVAR模型的最佳线性预测因子，并与非分式模型进行了样本外预测比较。通过对17个商品市场的日数据进行实证分析，发现基于预测比较统计指标的分数阶模型在样本内拟合和样本外预测方面都具有优势。我们通过一个基于加权的投资组合的动态交易策略来分析这些预测的经济意义。尽管在商品市场中存在着很大的异质性，但该分析在大多数市场中都带来了统计上显著的、经济上有意义的利润，并表明，从分数模型和非分数模型中获得的利润平均而言都比从简单移动平均策略中获得的利润更高，在统计上也更显著。分析还表明，尽管分式模型具有统计学上的优势，但分式模型和非分式模型产生的利润非常相似，平均上只比分式模型有微弱的优势。
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