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

Author

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  • Donggyu Kim

    (Department of Economics, University of California Riverside)

  • Minseog Oh
  • Yazhen Wang

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.

Suggested Citation

  • Donggyu Kim & Minseog Oh & Yazhen Wang, 2024. "Robust Realized Integrated Beta Estimator with Application to Dynamic Analysis of Integrated Beta," Working Papers 202422, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202422
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    References listed on IDEAS

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    1. Donggyu Kim & Minseok Shin, 2024. "Nonconvex High-Dimensional Time-Varying Coefficient Estimation for Noisy High-Frequency Observations with a Factor Structure," Working Papers 202418, University of California at Riverside, Department of Economics.

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