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arXiv · 2204.06914

Dynamic Realized Beta Models Using Robust Realized Integrated Beta Estimator

Donggyu Kim·Minseog Oh·Minjeong Song·Yazhen Wang
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Abstract

This paper introduces a unified parametric modeling approach for time-varying market betas that can accommodate continuous-time diffusion and discrete-time series models based on a continuous-time series regression model to better capture the dynamic evolution of market betas. We call this the dynamic realized beta (DR Beta). We first develop a 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, and construct the estimator's asymptotic properties. Then, with the robust realized integrated beta estimator, we propose a quasi-likelihood procedure for estimating the model parameters based on the combined high-frequency data and low frequency dynamic structure. 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 effectively accounts for dynamics in the market beta of individual stocks and better predicts future market betas.

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Donggyu Kim, Minseog Oh, Minjeong Song, Yazhen Wang. 2022-04-14. Dynamic Realized Beta Models Using Robust Realized Integrated Beta Estimator. https://arxiv.org/abs/2204.06914

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