Long memory volatility time series models


gdli - Posted on 08 February 2012

Project Description: 

The long run dependence phenomena can be observed in many financial time series and the long memory volatility time series model play the important role in this area. This project tries to develop some new model to explain such phenomena better.

Researcher name: 
Muyi Li
Researcher position: 
Xiamen University
Researcher email: 
Research Project Details
Project Duration: 
01/2011 to 01/2014
Project Significance: 
These new time series models are supposed to provide a better fit to the real data comparing with the current models.
Results Achieved: 
Li, M., Li, G. and Li, W.K. (2011) Score tests for hyperbolic GARCH models, Journal of Business & Economic Statistics 29, 579-586. Kwan, W., Li, W.K. and Li, G. (2011) On the threshold hyperbolic GARCH models, Statistics and Its Interface 4, 159-166. Li, G. and Li, W.K. (2011) Testing a linear time series models against its threshold extension, Biometrika 98, 243-250. Kwan, W., Li, W.K. and Li, G. (2010) On the estimation and diagnostic checking of the ARFIMA–HYGARCH model, Computational Statistics and Data Analysis. Accepted.