Estimation of α, β and portfolio weights in a pure-jump model with long memory in volatility

Yichen Zhang and Clifford Hurvich

Stochastic Processes and their Applications, 2022, vol. 150, issue C, 972-994

Abstract: We investigate a bivariate pure-jump model of stock prices with long memory in volatility, using a marked log-Gaussian Cox process. We show that, due to the non-synchronicity of transactions, the ordinary least squares estimator of the slope in a contemporaneous regression of returns on returns converges to different targets depending on the sampling frequency. Therefore, we propose a transaction-level estimator that makes full use of data in the complete continuous-time record, and show that the estimator of the slope has slow convergence with rate determined by the memory parameter in volatility.

Keywords: Bivariate pure-jump model; Long memory; Slow convergence; Log Gaussian Cox process (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.spa.2020.09.005

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