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D. Raggi and S. Bordignon, “Comparing Stochastic Volatility Models through Monte Carlo Simulations,” Computational Statistics & Data Analysis, Vol. 50, No. 7, 2006, pp. 1678-1699.
http://dx.doi.org/10.1016/j.csda.2005.02.004

has been cited by the following article:

• TITLE: Bayesian Estimation of Non-Gaussian Stochastic Volatility Models

  AUTHORS: Asma Graja Elabed, Afif Masmoudi

  KEYWORDS: on-Gaussian Distribution; Stochastic Volatility; Laplace Density; Fat Tails; Kullback Leiber Divengence; Bayesian Analysis; MCMC Algorithm

  JOURNAL NAME: Journal of Mathematical Finance, Vol.4 No.2, February 19, 2014

  ABSTRACT: In this paper, a general Non-Gaussian Stochastic Volatility model is proposed instead of the usual Gaussian model largely studied. We consider a new specification of SV model where the innovations of the return process have centered non-Gaussian error distribution rather than the standard Gaussian distribution usually employed. The model describes the behaviour of random time fluctuations in stock prices observed in the financial markets. It offers a response to better model the heavy tails and the abrupt changes observed in financial time series. We consider the Laplace density as a special case of non-Gaussian SV models to be applied to our data base. Markov Chain Monte Carlo technique, based on the bayesian analysis, has been employed to estimate the model’s parameters.