Dynamic Realized Beta Models Using Robust Realized Integrated Beta Estimator

TL;DR

This paper develops a robust, non-parametric estimator for dynamic market betas using high-frequency data, and introduces a model that captures their time-varying nature for improved prediction.

Contribution

It proposes a unified parametric approach for modeling time-varying market betas with a robust realized integrated beta estimator and a quasi-likelihood estimation method.

Findings

The estimator is robust to microstructure noise and stylized features.

The proposed model accurately captures beta dynamics.

Empirical results show improved beta prediction for stocks.

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.