In this paper, we develop a robust non-parametric realized integrated beta estimator using high-frequency financial data contaminated by microstructure noises, which is robust to the stylized features, such as the time-varying beta and the dependence structure of microstructure noises. With this robust realized integrated beta estimator, we investigate dynamic structures of integrated betas and find an auto-regressive--moving-average (ARMA) structure. To model this dynamic structure, we utilize the ARMA model for daily integrated market betas. We call this the dynamic realized beta (DR Beta). We further introduce a high-frequency data generating process by filling the gap between the high-frequency-based non-parametric estimator and low-frequency dynamic structure. Then, we propose a quasi-likelihood procedure for estimating the model parameters with the robust realized integrated beta estimator as the proxy. We also establish asymptotic theorems for the proposed estimator and conduct a simulation study to check the performance of finite samples of the estimator. The empirical study with the S&P 500 index and the top 50 large trading volume stocks from the S&P 500 illustrates that the proposed DR Beta model with the robust realized beta estimator effectively accounts for dynamics in the market beta of individual stocks and better predicts future market betas.
翻译:在本文中,我们利用受到微结构噪音污染的高频金融数据开发一个强健的、非参数化的一体化贝塔估计仪,这些数据对于结构化的特征,例如基于高频的非参数估测仪和微结构噪音依赖性结构,具有很强的综合性贝塔估测仪,我们利用受到微结构噪音污染的高频金融数据开发出一个强势、非参数化的综合贝塔估计仪。然后,我们利用这种强大的综合贝塔估计仪(ARMA)结构来调查综合贝塔的动态结构,并找到一个自动回退平均(ARMA)结构。为了模拟这种动态结构,我们使用ARMA模型进行日常集成市场集成市场测试。我们进一步引入高频数据生成程序,填补基于高频的非参数估测算仪与低频动态结构之间的差距。然后,我们提出一个准类似程序,用以估算模型参数,同时用已实现的贝塔图集集集集测算仪,并进行模拟研究,以测试每个估测算器(DR Beta)的定标样品的性性性工作,我们用S & Preal Stabial develop Stal Stal develop IS 和S & SBetal Stild Stild Stild Stild Stild Stild Stems 和S和S & sevd Stild Stild Stild Stild Stild Stildald Stildaldaldaldaldals 和S和S和S和S和S和SDBIS的50号最高序号进行实算算算算。