摘要翻译：
在多变量股票日收益率的随机波动模型中，随着收益率维数的增加，参数估计变得不稳定。为了解决这一问题，我们将重点放在多重收益的因子结构上，并考虑了两个额外的信息来源：一是与市场因子相关联的已实现股票指数，二是由高频数据计算的已实现协方差矩阵。将所提出的考虑杠杆效应和实现措施的动态因子模型应用于与SP500指数投资收益挂钩的交易所交易基金中的10只股票，结果表明该模型在投资组合绩效方面具有稳定的优势。
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英文标题：
《Dynamic factor, leverage and realized covariances in multivariate
stochastic volatility》
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作者：
Yuta Yamauchi and Yasuhiro Omori
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最新提交年份：
2021
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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：
In the stochastic volatility models for multivariate daily stock returns, it has been found that the estimates of parameters become unstable as the dimension of returns increases. To solve this problem, we focus on the factor structure of multiple returns and consider two additional sources of information: first, the realized stock index associated with the market factor, and second, the realized covariance matrix calculated from high frequency data. The proposed dynamic factor model with the leverage effect and realized measures is applied to ten of the top stocks composing the exchange traded fund linked with the investment return of the SP500 index and the model is shown to have a stable advantage in portfolio performance.
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