Alternative Asymmetric Stochastic Volatility Models
AbstractThe stochastic volatility model usually incorporates asymmetric effects by introducing the negative correlation between the innovations in returns and volatility. In this paper, we propose a new asymmetric stochastic volatility model, based on the leverage and size effects. The model is a generalization of the exponential GARCH (EGARCH) model of Nelson (1991). We consider categories for asymmetric effects, which describes the difference among the asymmetric effect of the EGARCH model, the threshold effects indicator function of Glosten, Jagannathan and Runkle (1992), and the negative correlation between the innovations in returns and volatility. The new model is estimated by the efficient importance sampling method of Liesenfeld and Richard (2003), and the finite sample properties of the estimator are investigated using numerical simulations. Four financial time series are used to estimate the alternative asymmetric SV models, with empirical asymmetric effects found to be statistically significant in each case. The empirical results for S&P 500 and Yen/USD returns indicate that the leverage and size effects are significant, supporting the general model. For TOPIX and USD/AUD returns, the size effect is insignificant, favoring the negative correlation between the innovations in returns and volatility. We also consider standardized t distribution for capturing the tail behavior. The results for Yen/USD returns show that the model is correctly specified, while the results for three other data sets suggest there is scope for improvement.
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Bibliographic InfoPaper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 10/70.
Length: 26 pages
Date of creation: 01 Nov 2010
Date of revision:
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Stochastic volatility; asymmetric effects; leverage; threshold; indicator function; importance sampling; numerical simulations;
Other versions of this item:
- Manabu Asai & Michael McAleer, 2011. "Alternative Asymmetric Stochastic Volatility Models," Econometric Reviews, Taylor and Francis Journals, vol. 30(5), pages 548-564.
- Manabu Asai & Michael McAleer, 2009. "Alternative Asymmetric Stochastic Volatility Models," CIRJE F-Series CIRJE-F-655, CIRJE, Faculty of Economics, University of Tokyo.
- Manabu Asai & Michael McAleer, 2010. "Alternative Asymmetric Stochastic Volatility Models," KIER Working Papers 739, Kyoto University, Institute of Economic Research.
- Asai, M. & McAleer, M.J., 2010. "Alternative Asymmetric Stochastic Volatility Models," Econometric Institute Report EI 2010-69, Erasmus University Rotterdam, Econometric Institute.
- Manabu Asai & Michael McAleer, 2009. "Alternative Asymmetric Stochastic Volatility Models," CARF F-Series CARF-F-166, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
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- Fernández, C. & Steel, M.F.J., 1996. "On Bayesian Modelling of Fat Tails and Skewness," Discussion Paper 1996-58, Tilburg University, Center for Economic Research.
- Xiuping Mao & Esther Ruiz & Helena Veiga, 2013. "One for all : nesting asymmetric stochastic volatility models," Statistics and Econometrics Working Papers ws131110, Universidad Carlos III, Departamento de Estadística y Econometría.
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