Multivariate stochastic volatility model with realized volatilities and pairwise realized correlations
Multivariate stochastic volatility model with realized volatilities and pairwise realized correlations
复制标题
具有已实现波动率和成对已实现相关性的多元随机波动率模型
DOI：
10.1080/07350015.2019.1602048
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发表时间：
2019
影响因子：
3
通讯作者：
Yuta Yamauchi and Yasuhiro Omori
中科院分区：
文献类型：
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作者：
Okazaki Tetsuji;Onishi Ken;Wakamori Naoki;末近浩太;Yuta Yamauchi and Yasuhiro Omori
虽然随机波动率和广义自回归条件异方差(GARCH)模型已经成功地描述了单变量资产收益率的波动率动态，但由于几个主要问题，将它们扩展到具有动态相关性的多变量模型是困难的。首先，如果可用数据只是每日收益，那么需要估计的参数太多，这导致估计不稳定。这个问题的一个解决方案是纳入基于日内资产回报的额外观察，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，我们必须使用最大时间间隔，以便观察所有资产收益来计算已实现的协方差矩阵。然而，在本研究中，当存在交易频率较低的资产时，我们没有充分利用可用的盘中信息。第三，保证估计的(和已实现的)协方差矩阵是正定的并非易事。我们的贡献如下：(1)利用已实现的度量得到动态相关模型的稳定参数估计；(2)充分利用日内信息；(3)保证协方差矩阵是正定的；(4)我们避免了资产收益排序的任意性；(5)我们提出了灵活的相关结构模型(例如，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性规范。我们提出的模型被应用于9只美国股票的日收益及其已实现波动性和成对已实现相关性，并被证明在投资组合表现方面优于已有的模型。
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 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.