A1017
Title: Robust realized integrated beta estimator with application to dynamic analysis of integrated beta
Authors: Minseog Oh - KAIST (Korea, South) [presenting]
Donggyu Kim - KAIST (Korea, South)
Yazhen Wang - University of Wisconsin (United States)
Abstract: A robust non-parametric realized integrated beta estimator is developed 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, dynamic structures of integrated betas are investigated, and an auto-regressive-moving-average (ARMA) structure is found. The ARMA model for daily integrated market betas is utilized to model this dynamic structure. This is called the dynamic realized beta (DR Beta). Further, a high-frequency data-generating process is introduced by filling the gap between the high-frequency-based non-parametric estimator and low-frequency dynamic structure. Then, a quasi-likelihood procedure for estimating the model parameters with the robust realized integrated beta estimator as the proxy is proposed. Asymptotic theorems are also established 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.
