Volatility clustering and herding agents: Does it matter what they observe?

Ryuichi Yamamoto*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Recent agent-based models have demonstrated that agents' herding behavior causes volatility clustering in stock markets. We examine economies where agents herd on others, yet they have limited sets of information on other agents to imitate. In particular, we conduct experiments on economies with agents with different levels of information sharing where agents can imitate: (1) the strategies of others but with an error, (2) the strategies of only a fraction of agents, or (3) the strategies of others, but update their parameters only by a proportion. In each experiment we change the likelihood that agents make errors to copy the strategy of others, the fraction of agents to herd, or the proportion of the parameter that agents update, in order to examine the effect of the different degrees of information sharing on volatility clustering. We show that volatility clustering tends to disappear when agents have limited information on the strategies of others, and agents need to imitate the strategy details of others in order to generate the clustered volatility.

Original languageEnglish
Pages (from-to)41-59
Number of pages19
JournalJournal of Economic Interaction and Coordination
Issue number1
Publication statusPublished - 2011 May 1
Externally publishedYes


  • Agent-based
  • Herding
  • Learning
  • Volatility clustering

ASJC Scopus subject areas

  • Business and International Management
  • Economics and Econometrics


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