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首页> 外文期刊>The North American journal of economics and finance >Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory
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Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory

机译：拉丁美洲股票和外汇市场波动建模：具有随机水平变动和真正的长期记忆的模型的经验应用

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Following Varneskov and Perron (2017a,b), I apply the RLS-ARFIMA(p,d,q) models to the daily stock and Forex market returns volatility of Argentina, Brazil, Mexico and Peru. Further, two sets of high-frequency data are also used. The model is a parametric state space model with an estimation framework that combines long memory and level shifts by decomposing the underlying process into a mixture model and ARFIMA dynamics. The results of the estimates are not conclusive as those obtained in Varneskov and Perron (2017a,b). In fact, the very small magnitudes of the fractional parameter estimates suggest that only the high-frequency series could be modeled as RLS-ARFIMA models. The other (daily) series would be modeled as RLS-ARMA models with measurement errors except in the case of the Forex market of Brazil where there is no evidence of measurement errors. Another possibility is to accept the small magnitudes of the estimates of the fractional parameter as evidence of genuine long memory and, in that case, a larger group of series can be modeled as RLS-ARFIMA models. The forecasts are evaluated from two perspectives: one using 10% of the Model Confidence Set of Hansen, Lunde, and Nason (2011) and the other using a recent statistic proposed by Kniippel (2015) to evaluate density forecasts. The results favor the RLS-ARMA and RLS-ARFIMA models although we found some differences between the two approaches for the cases of Brazil and Mexico (stocks) and Argentina (Forex). Finally, forecasts are used to calculate the VaR at 1%, 5% and 10%. The results support broadly the RLS-ARFIMA models with one or two exceptions. (C) 2017 Elsevier Inc. All rights reserved.
机译：根据Varneskov和Perron（2017a，b），我将RLS-ARFIMA（p，d，q）模型应用于阿根廷，巴西，墨西哥和秘鲁的每日股票和外汇市场收益波动率。此外，还使用了两组高频数据。该模型是带有估计框架的参数状态空间模型，该框架通过将基础过程分解为混合模型和ARFIMA动态来结合长内存和级别移位。估计结果并不像在Varneskov和Perron（2017a，b）中得出的结论一样。实际上，分数参数估计值的很小幅度表明，只有高频序列可以建模为RLS-ARFIMA模型。其他（每日）系列将被建模为具有测量误差的RLS-ARMA模型，除非在巴西的外汇市场中没有测量误差的证据。另一种可能性是接受分数参数估计值的小幅值作为真正长记忆的证据，在这种情况下，可以将更大系列的序列建模为RLS-ARFIMA模型。预测从两个角度进行评估：一个使用Hansen，Lunde和Nason（2011）的模型置信度集的10％，另一个使用Kniippel（2015）提出的最新统计数据来评估密度预测。尽管我们发现两种方法在巴西和墨西哥（股票）和阿根廷（外汇）的案例之间存在一些差异，但结果支持RLS-ARMA和RLS-ARFIMA模型。最后，使用预测来计算VaR分别为1％，5％和10％。结果广泛支持RLS-ARFIMA模型，但有一个或两个例外。 （C）2017 Elsevier Inc.保留所有权利。

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