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首页> 外文期刊>The quarterly review of economics and finance >Long memory and structural breaks in modeling the return and volatility dynamics of precious metals
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Long memory and structural breaks in modeling the return and volatility dynamics of precious metals

机译：在对贵金属的收益率和波动率动力学进行建模时需要较长的记忆力和结构性突破

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摘要

We investigate the potential of structural changes and long memory (LM) properties in returns and volatility of the four major precious metal commodities traded on the COMEX markets (gold, silver, platinum and palladium). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semi-parametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the precious metals. Moreover, for most of the precious metals considered, this dual long memory is found to be adequately captured by an ARFIMA-F1GARCH model, which also provides better out-of-sample forecast accuracy than several popular volatility models. Finally, evidence shows that conditional volatility of precious metals is better explained by long memory than by structural breaks.
机译：我们研究了在COMEX市场上交易的四种主要贵金属商品（金，银，铂和钯）的收益率和波动率中结构变化和长记忆（LM）特性的潜力。广义上讲，如果随机变量的自相关函数不可积分，则称其表现出较长的记忆行为，而结构变化会导致该变量的时间序列行为发生突然的显着变化。实施几种参数和半参数方法的结果表明，在日常条件下，贵金属的收益率和波动率过程具有长期依赖性。此外，对于考虑的大多数贵金属，ARFIMA-F1GARCH模型可以充分捕获这种双重长时记忆，与几种流行的波动率模型相比，该模型还提供了更好的样本外预测准确性。最后，证据表明，长期记忆比结构性断裂更好地解释了贵金属的条件波动性。

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