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首页> 外文期刊>Petroleum science >Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach
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Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach

机译：使用一类非线性波动率模型预测油价的波动性：平稳过渡RBF和MLP神经网络增强的GARCH方法

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In this study, the forecasting capabilities of a new class of nonlinear econometric models, namely, the LSTAR-LST-GARCH-RBF and MLP models are evaluated. The models are utilized to model and to forecast the daily returns of crude oil prices. Many financial time series are subjected to leptokurtic distribution, heavy tails, and nonlinear conditional volatility. This characteristic feature leads to deterioration in the forecast capabilities of traditional models such as the ARCH and GARCH models. According to the empirical findings, the oil prices and their daily returns could be classified as possessing nonlinearity in the conditional mean and conditional variance processes. Several model groups are evaluated: (i) the models proposed in the first group are the LSTAR-LST-GARCH models that are augmented with fractional integration and asymmetric power terms (FIGARCH, APGARCH, and FIAPGARCH); (ii) the models proposed in the second group are the LSTAR-LST-GARCH models further augmented with MLP and RBF type neural networks. The models are compared in terms of MSE, RMSE, and MAE criteria for in-sample and out-of-sample forecast capabilities. The results show that the LSTAR based and neural network augmented models provide important gains over the single-regime baseline GARCH models, followed by the LSTAR-LST-GARCH type models in terms of modeling and forecasting volatility in crude oil prices.
机译：在这项研究中，评估了新型非线性计量经济模型，即LSTAR-LST-GARCH-RBF和MLP模型的预测能力。这些模型用于建模和预测原油价格的每日收益。许多金融时间序列都受到leptokurtic分布，重尾和非线性条件波动的影响。此特征会导致传统模型（例如ARCH和GARCH模型）的预测能力下降。根据经验发现，在条件均值和条件方差过程中，油价及其日收益率可以归类为具有非线性。对几个模型组进行了评估：（i）第一组中提出的模型是LSTAR-LST-GARCH模型，该模型通过分数积分和不对称幂项进行了扩充（FIGARCH，APGARCH和FIAPGARCH）; （ii）第二组中提出的模型是进一步用MLP和RBF型神经网络增强的LSTAR-LST-GARCH模型。针对MSE，RMSE和MAE标准对模型进行了比较，以提供样本内和样本外预测功能。结果表明，在建模和预测原油价格波动方面，基于LSTAR的模型和基于神经网络的增强模型比单域基准GARCH模型具有重要优势，其次是LSTAR-LST-GARCH类型模型。

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