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摘要翻译：
虽然随机波动率模型和GARCH（广义自回归条件异方差）模型成功地描述了单变量资产收益的波动动力学，但由于存在几个主要问题，将它们推广到具有动态相关性的多元资产收益模型一直很困难。首先，如果可用的数据仅为每日回报，则有太多的参数需要估计，这导致估计不稳定。解决这个问题的一个办法是根据日内资产收益，加入额外的观察，例如已实现的协方差。其次，由于多元资产收益不是同步交易的，为了计算实现的协方差矩阵，我们必须使用最大的时间间隔来观察所有资产收益。然而，在本研究中，当交易频率较低的资产时，我们未能充分利用可用的日内信息。第三，保证估计的（和实现的）协方差矩阵是正定的并不简单。我们的贡献如下：（1）利用实现测度得到了动态相关模型的稳定参数估计；（2）利用两两实现相关充分利用了日内信息；（3）保证了协方差矩阵的正定；（4）避免了资产收益排序的任意性；（5）提出了灵活的相关结构模型（如在必要时将某些相关性设置为零）；（6）提出了杠杆效应的简约规范。我们提出的模型被应用于9只美国股票的日收益，它们的实现挥发和成对实现相关，并显示在投资组合绩效方面优于现有的模型。
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英文标题：
《Multivariate Stochastic Volatility Model with Realized Volatilities and
Pairwise Realized Correlations》
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作者：
Yuta Yamauchi and Yasuhiro Omori
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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：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
Although stochastic volatility and GARCH (generalized autoregressive conditional heteroscedasticity) models have successfully described the volatility dynamics of univariate asset returns, extending them to the multivariate models with dynamic correlations has been difficult due to several major problems. First, there are too many parameters to estimate if available data are only daily returns, which results in unstable estimates. One solution to this problem is to incorporate additional observations based on intraday asset returns, such as realized covariances. Second, since multivariate asset returns are not synchronously traded, we have to use the largest time intervals such that all asset returns are observed in order to compute the realized covariance matrices. However, in this study, we fail to make full use of the available intraday informations when there are less frequently traded assets. Third, it is not straightforward to guarantee that the estimated (and the realized) covariance matrices are positive definite. Our contributions are the following: (1) we obtain the stable parameter estimates for the dynamic correlation models using the realized measures, (2) we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5) we propose the flexible correlation structure model (e.g., such as setting some correlations to be zero if necessary), and (6) the parsimonious specification for the leverage effect is proposed. Our proposed models are applied to the daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations and are shown to outperform the existing models with respect to portfolio performances.
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PDF链接：
https://arxiv.org/pdf/1809.09928
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