Exportar Publicação

Tavares, A. B., Curto, J. D. & Tavares, G. N. (2008). Modelling heavy tails and asymmetry using ARCH-type models with stable Paretian distributions. Nonlinear Dynamics. 51 (1-2), 231-243

T. A.B. et al.,  "Modelling heavy tails and asymmetry using ARCH-type models with stable Paretian distributions", in Nonlinear Dynamics, vol. 51, no. 1-2, pp. 231-243, 2008

@article{a.b.2008_1714937443648,
	author = "Tavares, A. B. and Curto, J. D. and Tavares, G. N.",
	title = "Modelling heavy tails and asymmetry using ARCH-type models with stable Paretian distributions",
	journal = "Nonlinear Dynamics",
	year = "2008",
	volume = "51",
	number = "1-2",
	doi = "10.1007/s11071-007-9206-5",
	pages = "231-243",
	url = "https://link.springer.com/article/10.1007%2Fs11071-007-9206-5"
}

TY  - JOUR
TI  - Modelling heavy tails and asymmetry using ARCH-type models with stable Paretian distributions
T2  - Nonlinear Dynamics
VL  - 51
IS  - 1-2
AU  - Tavares, A. B.
AU  - Curto, J. D.
AU  - Tavares, G. N.
PY  - 2008
SP  - 231-243
SN  - 0924-090X
DO  - 10.1007/s11071-007-9206-5
UR  - https://link.springer.com/article/10.1007%2Fs11071-007-9206-5
AB  - Several approaches have been considered to model the heavy tails and asymmetric effect on stocks returns volatility. The most commonly used models are the Exponential Generalized Auto-Regressive Conditional Heteroskedasticity (EGARCH), the Threshold GARCH (TGARCH), and the Asymmetric Power ARCH (APARCH) which, in their original form, assume a Gaussian distribution for the innovations. In this paper we propose the estimation of all these asymmetric models on empirical distributions of the Standard & Poor's (S&P) 500 and the Financial Times Stock Exchange (FTSE) 100 daily returns, assuming the Student's t and the stable Paretian (with a < 2) distributions for innovations. To the authors' best knowledge, analysis of the EGARCH and TGARCH assuming innovations with a-stable distribution have not yet been reported in the literature. The results suggest that this kind of distributions clearly outperforms the Gaussian case. However, when a-stable and Student's t distributions are compared, a general conclusion should be avoided as the goodness-of-fit measures favor the astable distribution in the case of S&P 500 returns and the Student's t distribution in the case of FTSE 100.
ER  -