论文标题
复发条件异方差
Recurrent Conditional Heteroskedasticity
论文作者
论文摘要
我们提出了一类新的财务波动模型，称为复发性有条件异质（RECH）模型，以改善对传统有条件异质性杂质模型的样本内分析和样本外预测。特别是，我们将辅助确定性过程（由经常性神经网络支配）纳入传统条件异质模型的条件差异，例如GARCH型模型，以灵活地捕获潜在波动的动力学。 RECH模型可以检测到现有条件异质模型（例如Garch，GJR和Egarch）忽略的财务波动中有趣的效果。新模型通常具有良好的样本预测，同时仍然通过保留经济学加尔奇型模型的良好特征来很好地解释财务波动的风格化事实。这些特性通过模拟研究和对31个库存指数和汇率数据的应用进行说明。 。用户友好的软件包以及本文中报道的示例可在https://github.com/vbayeslab上找到。
We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in-sample analysis and out-ofsample forecasting of the traditional conditional heteroskedastic models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into the conditional variance of the traditional conditional heteroskedastic models, e.g. GARCH-type models, to flexibly capture the dynamics of the underlying volatility. RECH models can detect interesting effects in financial volatility overlooked by the existing conditional heteroskedastic models such as the GARCH, GJR and EGARCH. The new models often have good out-of-sample forecasts while still explaining well the stylized facts of financial volatility by retaining the well-established features of econometric GARCH-type models. These properties are illustrated through simulation studies and applications to thirty-one stock indices and exchange rate data. . An user-friendly software package together with the examples reported in the paper are available at https://github.com/vbayeslab.
