Volatility Clustering: A Nonlinear Theoretical Approach
Xuezhong He (),
Kai Li and
No 365, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney
This paper verifies the endogenous mechanism and economic intuition on volatility clustering using the coexistence of two locally stable attractors proposed by Gaunersdorfer, Hommes and Wagener (2008). By considering a simple asset pricing model with two types of boundedly rational traders, fundamentalists and trend followers, and noise traders, we provide conditions on the coexistence of locally stable steady state and invariant cycle of the underlying nonlinear deterministic financial market model and show numerically that the interaction of the coexistence of the deterministic dynamics and noise processes can endogenously generate volatility clustering and long range dependence in volatility observed in financial markets. Economically, volatility clustering occurs when neither the fundamental nor trend following traders dominate the market and when traders switch more often between the two strategies.
Keywords: volatility clustering; fundamentalists and trend followers; bounded rationality; stability, coexisting attractors (search for similar items in EconPapers)
JEL-codes: D84 E32 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Published as: He, X., Li, K. and Wang, C., 2016, "Volatility Clustering: A Nonlinear Theoretical Approach", Journal of Economic Behavior and Organization, 130, 274-297.
Downloads: (external link)
Journal Article: Volatility clustering: A nonlinear theoretical approach (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:uts:rpaper:365
Access Statistics for this paper
More papers in Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney PO Box 123, Broadway, NSW 2007, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Duncan Ford ().