摘要翻译：
为了改善传统条件异方差模型的样本内分析和样本外预测性能，我们提出了一类新的金融波动模型，我们称之为递归条件异方差(RECH)模型。特别地，我们将由递归神经网络控制的辅助确定性过程引入传统的条件异方差模型（如GARCH型模型）的条件方差中，以灵活地捕捉底层波动率的动态。RECH模型可以发现金融波动中被现有的条件异方差模型如GARCH(Bollerslev，1986)、GJR(Glosten et al.，1993)和EGARCH(Nelson，1991)所忽略的有趣的影响。新的模型通常有很好的样本外预测，同时仍然通过保留计量经济学GARCH型模型的既定结构来很好地解释金融波动的程式化事实。通过对四个实际股票指数数据集的仿真研究和应用，说明了这些性质。在https://github.com/vbayeslab上提供了一个用户友好的软件包以及本文报告的示例。
---
英文标题：
《Recurrent Conditional Heteroskedasticity》
---
作者：
T.-N. Nguyen, M.-N. Tran, and R. Kohn
---
最新提交年份：
2020
---
分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
英文摘要：
  We propose a new class of financial volatility models, which we call the REcurrent Conditional Heteroskedastic (RECH) models, to improve both the in-sample analysis and out-of-sample forecast performance 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. the GARCH-type models, to flexibly capture the dynamics of the underlying volatility. The RECH models can detect interesting effects in financial volatility overlooked by the existing conditional heteroskedastic models such as the GARCH (Bollerslev, 1986), GJR (Glosten et al., 1993) and EGARCH (Nelson, 1991). The new models often have good out-of-sample forecasts while still explain well the stylized facts of financial volatility by retaining the well-established structures of the econometric GARCH-type models. These properties are illustrated through simulation studies and applications to four real stock index datasets. An user-friendly software package together with the examples reported in the paper are available at https://github.com/vbayeslab.
---
PDF链接：
https://arxiv.org/pdf/2010.13061