Mixture autoregressive models as viable alternatives to some GARCH models in predicting VaR and ES for selected Nigerian stocks

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Original language	English
Title of host publication	Unilag conference proceedings, 16/SCI/174
Pages	522-534
Number of pages	13
Publication status	Published - 2016

Cite this

@inproceedings{b17e5a8ca22d45fb91b11d877e23c764,

title = "Mixture autoregressive models as viable alternatives to some GARCH models in predicting VaR and ES for selected Nigerian stocks",

author = "M.I Akinyemi and Boshnakov, {Georgi N.}",

year = "2016",

language = "English",

pages = "522--534",

booktitle = "Unilag conference proceedings, 16/SCI/174",

}