  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.
