Time-Varying Betas and Effects of Data Frequency and Estimation Window Preferences: Case of Istanbul Stock Exchange

Abstract

In this study, we analyzed the changes in Beta over time for the leading indexes of Borsa Istanbul (XU100, XUHIZ, XUMAL, XUSIN, XUTEK) across 5-10 year and 15-year intervals from 2008 to 2023. We utilized Rolling regression and Recursive regression methods to estimate the fluctuations in Beta over time and compared the performance of these estimation techniques. To evaluate the effect of the estimation window length on Beta, we incorporated daily and weekly estimation windows of various lengths: 252 days, 126 days, 52 weeks, and 26 weeks. Additionally, we examined how data frequency affects Beta estimation using daily and weekly datasets. Our analysis showed that the Rolling regression method consistently outperformed the recursive method. Moreover, we found that employing daily datasets, instead of monthly datasets, significantly enhanced Beta forecast performance. We also found that a 126-day window is the most effective length for the estimation window.