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摘要翻译：
通过对非均质自回归(HAR)模型中所涉及的四个部分波动项的联合分布进行建模，对模型进行了修正。即今天的、昨天的、上周的和上个月的波动性成分。联合分布依赖于一个（C-）Vine copula结构，允许方便地提取基于给定其过去条件的今天波动率的条件预期的波动率预测。本文的实证应用涉及十只股票七年多的高频交易价格，并评估了我们的模型对每日实现核心测度的样本内、样本外和一步预测的性能。本文所提出的模型在不同的边际分布模型、copula构造方法和预测设置下都优于HAR模型。
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英文标题：
《A Vine-copula extension for the HAR model》
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作者：
Martin Magris
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最新提交年份：
2019
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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。
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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
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英文摘要：
The heterogeneous autoregressive (HAR) model is revised by modeling the joint distribution of the four partial-volatility terms therein involved. Namely, today's, yesterday's, last week's and last month's volatility components. The joint distribution relies on a (C-) Vine copula construction, allowing to conveniently extract volatility forecasts based on the conditional expectation of today's volatility given its past terms. The proposed empirical application involves more than seven years of high-frequency transaction prices for ten stocks and evaluates the in-sample, out-of-sample and one-step-ahead forecast performance of our model for daily realized-kernel measures. The model proposed in this paper is shown to outperform the HAR counterpart under different models for marginal distributions, copula construction methods, and forecasting settings.
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PDF链接：
https://arxiv.org/pdf/1907.08522
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