A Transmission of Beta Herding during Subprime Crisis in Taiwan’s Market: DCC-MIDAS Approach

Yi-Chang Chen,Shih-Ming Kuo,Yuanyuan Zhang,Hung-Che Wu

doi:10.3390/ijfs9040070

Abstract

The aim of this study is to investigate the herding of beta transmission between return and volatility. We have used the dynamic conditional correlation model with the mixed-data sampling (DCC-MIDAS) model for the analysis. The evidence demonstrates that herding is a key transmitter in Taiwan’s stock market. The significant estimation of DCC-MIDAS explains that the herding phenomenon is highly dynamic and time-varying in herding behavior. By means of time-varying beta of herding based on our rolling forecasting method and robustness check of the Markov-switching regression approach using four types of portfolios, the evidence indicates that there are conditional correlations between betas and herding. In addition, it also reveals that herding forms in Taiwan’s markets during the subprime crisis period.

Highlights

Herding is a decision-making behavior in the financial market
Fei and Liu (2021) explore the impact of investor herding behavior on stock market volatility, and find evidence that the information contained in the herding measure helps improve volatility forecasts and economic value to investors
According to the results proposed by Brooks et al (1998)„ the Kalman filter is superior to the generalized autoregressive conditional heteroskedasticity (GARCH) using in-sample and out-of-sample return forecasts based on beta estimates

Summary

Introduction

Herding is a decision-making behavior in the financial market. Herding behavior is an important issue for investors’ sentiment, investment strategies and stock market stability (see Bouri et al 2021; Demirer et al 2019; Fei and Liu 2021; Guo et al 2020, for example). Previous studies have pointed out that herding has mixed evidence in the literature on the impact of stock market volatility. Shyu and Sun (2010) find no significant changes of institutional herding under market stress in the Taiwan stock market. Hsieh (2013) finds evidence of herding by both institutional and individual investors in the Taiwan stock market using high-frequency intraday data. Fei and Liu (2021) explore the impact of investor herding behavior on stock market volatility, and find evidence that the information contained in the herding measure helps improve volatility forecasts and economic value to investors

Objectives

Results

Conclusion