Long Memory, Heterogeneity, and Trend Chasing
Youwei Li and
Xuezhong (Tony) He ()
No 113, Computing in Economics and Finance 2005 from Society for Computational Economics
Abstract:
Long-range dependence in volatility is one of the most prominent examples of applications in financial market research involving universal power laws. Its characterization has recently spurred attempts at theoretical explanation of the underlying mechanism. This paper contributes to this recent development by analyzing a simple market fraction asset pricing model with two types of traders---fundamentalists who trade on the price deviation from estimated fundamental value and trend followers who follow a trend which is updated through a geometric learning process. Our analysis shows that the heterogeneity, trend chasing through learning, and the interplay of noisy processes and a stable deterministic equilibrium can be the source of power-law distributed fluctuations. Statistical analysis based on Monte Carlo simulations are conducted to characterize the long memory. Realistic estimates of the power-law decay indices and the (FI)GARCH parameters are presented
Keywords: Asset pricing; fundamentalists and trend followers; market fraction; stability; learning; long memory. (search for similar items in EconPapers)
JEL-codes: C10 D40 G12 (search for similar items in EconPapers)
Date: 2005-11-11
References: Add references at CitEc
Citations: View citations in EconPapers (2)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
Working Paper: Long Memory, Heterogeneity and Trend Chasing (2005) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf5:113
Access Statistics for this paper
More papers in Computing in Economics and Finance 2005 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().