This paper examines the relationship between volatility and herd behavior, which has been a growing
issue in financial economics and econophysics. Relaxing an unrealistic assumption of a representative
percolation model, this paper theoretically demonstrates that herd behavior leads to a high increase in
volatility but not trading volume, in contrast with information flows that give rise to increases in both
volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its
empirical difficulty, this paper proposes a new methodology for detecting trading days with herding
based upon the theoretical results from the percolation model and the concepts of both realized
volatility and realized bipower variation. Furthermore, this paper suggests a herd-behavior-stochastic-
volatility model, which accounts for herding in financial markets, and considers a Markov chain
Monte Carlo method as an efficient method for estimating the model. Strong evidence in favor of the
model specification over the standard stochastic volatility models is based on empirical application
with high frequency data in the Korean equity market, strongly supporting the theoretical intuition
that herd behavior causes excess volatility. In addition, this research indicates that strong persistence
in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior
rather than news.
Keywords: Herd behavior, Realized volatility, Realized bipower variation, Herd-behavior-stochastic-
volatility model, Markov Chain Monte Carlo (MCMC), Spline regression
학술대회/행사
학술대회안내
[2008년 제 4차] Herd Behavior and Volatility in Financial Markets
작성자 : 관리자
조회수 : 1049
게시일 :
2008-12-11