Robust Realized Integrated Beta Estimator with Application to Dynamic Analysis of Integrated Beta

Donggyu Kim (), Minseog Oh and Yazhen Wang
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Donggyu Kim: Department of Economics, University of California Riverside

No 202422, Working Papers from University of California at Riverside, Department of Economics

Abstract: In this paper, we develop a robust non-parametric realized integrated beta estimator using high-frequency financial data contaminated by microstructure noise, which is robust to the stylized features, such as the time-varying beta and the price-dependent and autocorrelated microstructure noise. With this robust realized integrated beta estimator, we investigate dynamic structures of integrated betas and find a persistent autoregressive structure. To model this dynamic structure, we utilize the autoregressivemoving-average (ARMA) model for daily integrated market betas. We call this the dynamic realized beta (DR Beta). Then, we propose a quasi-likelihood procedure for estimating the parameters of the ARMA model with the robust realized integrated beta estimator as the proxy. We establish asymptotic theorems for the proposed estimator and conduct a simulation study to check the performance of finite samples of the estimator. The proposed DR Beta model with the robust realized beta estimator is also illustrated by using data from the E-mini S&P 500 index futures and the top 50 large trading volume stocks from the S&P 500 and an application to constructing market-neutral portfolios.

Date: 2024-12
New Economics Papers: this item is included in nep-ecm and nep-mst
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