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Export Reference (APA) 
Grané, A. & Veiga, H. (2008). Accurate minimum capital risk requirements: a comparison of several approaches. Journal of Banking and Finance. 32 (11), 2482-2492
Export Reference (IEEE) 
A. Grané and M. H. Veiga,  "Accurate minimum capital risk requirements: a comparison of several approaches", in Journal of Banking and Finance, vol. 32, no. 11, pp. 2482-2492, 2008
Export BibTeX 
@article{grané2008_1766261482403,
	author = "Grané, A. and Veiga, H.",
	title = "Accurate minimum capital risk requirements: a comparison of several approaches",
	journal = "Journal of Banking and Finance",
	year = "2008",
	volume = "32",
	number = "11",
	doi = "10.1016/j.jbankfin.2008.05.003",
	pages = "2482-2492",
	url = "http://www.sciencedirect.com/science/article/pii/S0378426608000964?via%3Dihub"
}
Export RIS 
TY  - JOUR
TI  - Accurate minimum capital risk requirements: a comparison of several approaches
T2  - Journal of Banking and Finance
VL  - 32
IS  - 11
AU  - Grané, A.
AU  - Veiga, H.
PY  - 2008
SP  - 2482-2492
SN  - 0378-4266
DO  - 10.1016/j.jbankfin.2008.05.003
UR  - http://www.sciencedirect.com/science/article/pii/S0378426608000964?via%3Dihub
AB  - In this paper we estimate, for several investment horizons, minimum capital risk requirements for short and long positions, using the unconditional distribution of three daily indexes futures returns and a set of short and long memory stochastic volatility and GARCH-type models. We consider the possibility that errors follow a t-Student distribution in order to capture the kurtosis of the returns’ series. The results suggest that accurate modelling of extreme observations obtained for long and short trading investment positions is possible with an autoregressive stochastic volatility model. Moreover, modelling futures returns with a long memory stochastic volatility model produces, in general, excessive volatility persistence, and consequently, leads to large minimum capital risk requirement estimates. Finally, the models’ predictive ability is assessed with the help of out-of-sample conditional tests.
ER  -