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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Curto, J. & Pinto, J. (2012). Predicting the financial crisis volatility. Economic Computation And Economic Cybernetics Studies and Research Journal. 46 (1), 183-195
Exportar Referência (IEEE) 
J. J. Curto and J. C. Pinto,  "Predicting the financial crisis volatility", in Economic Computation And Economic Cybernetics Studies and Research Journal, vol. 46, no. 1, pp. 183-195, 2012
Exportar BibTeX 
@article{curto2012_1732199845985,
	author = "Curto, J. and Pinto, J.",
	title = "Predicting the financial crisis volatility",
	journal = "Economic Computation And Economic Cybernetics Studies and Research Journal",
	year = "2012",
	volume = "46",
	number = "1",
	pages = "183-195",
	url = "http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=78951349&site=ehost-live&scope=site"
}
Exportar RIS 
TY  - JOUR
TI  - Predicting the financial crisis volatility
T2  - Economic Computation And Economic Cybernetics Studies and Research Journal
VL  - 46
IS  - 1
AU  - Curto, J.
AU  - Pinto, J.
PY  - 2012
SP  - 183-195
SN  - 0424-267X
UR  - http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=78951349&site=ehost-live&scope=site
AB  - A volatility model must be able to forecast volatility even in extreme situations. Thus, the main objective of this paper, and due to the most recent increase in international stock markets' volatility, is to check which one of the most popular autoregressive conditional heteroskedasticity models (GARCH, GJR, EGARCH or APARCH) is more able to predict the extreme volatility in 2008 considering the daily returns of eight major international stock market indexes: CAC 40 (France), DAX 30 (Germany), FTSE 100 (UK), NIKKEI 225 (Japan), HANG SENG (Hong Kong), NASDAQ 100, DJIA and S&P 500 (United States). Goodness-of-fit measures demonstrate that EGARCH and APARCH models are able to correctly fit the conditional heteroskedasticity dynamics of the return's series under study. In terms of volatility forecast comparisons, using the Harvey-Newbold test for multiple forecasts encompassing and the ranking of forecasts based on the coefficient of determination (R-2) resulting from the Mincer-Zarnowitz regression, we conclude that EGARCH dominates competing standard asymmetric models.
ER  -