Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection.
Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection.
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DOI：
10.1080/01621459.2012.656041
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发表时间：
2012
影响因子：
3.7
通讯作者：
Yu K
中科院分区：
文献类型：
--
作者：
Fan J;Li Y;Yu K
基于总风险约束的投资组合分配是一种有效的方法，可以提高投资组合选择的效率和稳定性。所需的高维波动率矩阵可以通过使用高频金融数据来估计。这使得我们能够更好地适应大量资产之间的局部波动率和局部相关性，并显着增加估计波动率矩阵的样本量。本文从投资组合的角度研究了高维高频数据下波动率矩阵的估计问题。具体而言，我们提出了使用“成对刷新时间”和“所有刷新时间”的方法的基础上提出的“刷新时间”的概念，估计巨大的协方差矩阵，并比较它们的优点在投资组合选择。我们建立了估计的集中不等式，保证了在总暴露约束下的大规模资产配置中估计的波动率矩阵的理想性质。通过精心设计的模拟进行了广泛的数值研究。与基于低频日数据的方法相比，该方法能够捕捉到时变波动率和相关性的最新趋势，从而为下一时段的投资组合配置提供更准确的指导。在我们的模拟和实证研究中，使用高频数据的优势是显着的，其中包括50个模拟资产和道琼斯工业平均指数的30只成分股。
Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of portfolios selection among a vast pool of assets, as demonstrated in. The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enables us to better adapt to the local volatilities and local correlations among vast number of assets and to increase significantly the sample size for estimating the volatility matrix. This paper studies the volatility matrix estimation using high-dimensional high-frequency data from the perspective of portfolio selection. Specifically, we propose the use of “pairwise-refresh time” and “all-refresh time” methods based on the concept of “refresh time” proposed by for estimation of vast covariance matrix and compare their merits in the portfolio selection. We establish the concentration inequalities of the estimates, which guarantee desirable properties of the estimated volatility matrix in vast asset allocation with gross exposure constraints. Extensive numerical studies are made via carefully designed simulations. Comparing with the methods based on low frequency daily data, our methods can capture the most recent trend of the time varying volatility and correlation, hence provide more accurate guidance for the portfolio allocation in the next time period. The advantage of using high-frequency data is significant in our simulation and empirical studies, which consist of 50 simulated assets and 30 constituent stocks of Dow Jones Industrial Average index.
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影响因子：
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