Estimating and forecasting generalized fractional Long memory stochastic volatility models
Estimating and forecasting generalized fractional Long memory stochastic volatility models
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DOI：
10.3390/jrfm10040023
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
S. Peiris;Manabu Asai;M. McAleer
中科院分区：
文献类型：
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作者：
S. Peiris;Manabu Asai;M. McAleer
分数差分过程由于其在长记忆金融应用中的灵活性，近年来受到了广泛的关注。本文考虑一类由Gegenbauer多项式生成的模型，在随机波动率（SV）分量中引入长记忆性，建立了广义长记忆SV（GLMSV）模型。我们研究了新模型的统计性质，建议使用谱似然估计的长记忆过程，并通过Monte Carlo实验研究有限样本性质。我们将该模型应用于三个汇率收益率序列。总体而言，样本外预测的结果显示了新的GLMSV模型的充分性。
In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.