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.
Highlights
The Stochastic Volatility models have been widely used to model a changing variance of time series Financial data [1,2]
We consider a Stochastic Volatility model with a non-Gaussian noise where the return Yt follows a nonGaussian distribution with a mean equal zero and a variance governed by stochastic effects
The most popular non-Gaussian centered error distributions that has been applied in SV models are: Student distribution [3], Generalized Error Distribution [4], Mixture of Normal distribution [7], α -stable distribution [12], Laplace distribution, Uniform distribution....Among these nonGaussian centered error density, we have chosen the Laplace one for being applied to SV model
Summary
The Stochastic Volatility models have been widely used to model a changing variance of time series Financial data [1,2]. [7] considered a mixture of normal distribution as the error distribution in the SV model He used a bayesian method via MCMC technique to estimate the model’s parameters. If the data base presents a little dispersion measure, the non-Gaussian centered error specification will behave better than the Gaussian one. For this reason, we propose a general SV model where the diffusion of the stock return follows a non-Gaussian distribution. Finance has adopted a simple model, developed over the years, that attempts to describe the behaviour of random time fluctuation in the prices of stocks observed in the markets This model assumes that the fluctuation of the stock prices follow a log-normal probability distribution function. In order to consider a more general case of SV model, we propose a non-Gaussian centered error distribution for t
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