The motivation of this study is built from the previous research to find a way to enhance the forecast of advanced and emerging market currency volatilities. Given the exchange rate’s nonlinear and time-varying characteristics, we introduce the neural networks (NN) approach to enhance the Markov Switching Beta-Exponential Generalized Autoregressive Conditional Heteroscedasticity (MS-Beta-t-EGARCH) model. Our hybrid model synthesizes these two approaches’ advantages to predict exchange rate volatility. We validate the performance of our proposed model by comparing it with various traditional volatility forecasting models. In-sample and out-of-sample volatility forecasts are considered to achieve our comparison. The empirical results suggest that our hybrid NN-MS Beta-t-EGARCH outperforms the other models for both emerging and advanced market currencies.

中文翻译：

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通过混合神经网络马尔可夫切换 Beta-t-EGARCH 预测汇率波动

本研究的动机是建立在先前的研究之上，以寻找一种方法来增强对先进市场和新兴市场货币波动率的预测。鉴于汇率的非线性和时变特性，我们引入了神经网络 (NN) 方法来增强马尔可夫切换 Beta-指数广义自回归条件异方差 (MS-Beta-t-EGARCH) 模型。我们的混合模型综合了这两种方法在预测汇率波动方面的优势。我们通过将其与各种传统的波动率预测模型进行比较来验证我们提出的模型的性能。样本内和样本外波动率预测被认为是为了实现我们的比较。