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Bayesian Estimation of Non-Gaussian Stochastic Volatility Models

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DOI: 10.4236/jmf.2014.42009    4,418 Downloads   6,611 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

A. Elabed and A. Masmoudi, "Bayesian Estimation of Non-Gaussian Stochastic Volatility Models," Journal of Mathematical Finance, Vol. 4 No. 2, 2014, pp. 95-103. doi: 10.4236/jmf.2014.42009.

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