This paper examines two asymmetric stochastic volatility models used to describe the volatility dependencies found in most financial returns. The first is the autoregressive stochastic volatility model with Student’s t-distribution (ARSV-t), and the second is the basic SVOL of Jacquier et al. (J Bus Econ Stat 14:429–434, 1994). In order to estimate these models, our analysis is based on the Markov Chain Monte-Carlo (MCMC) method. Therefore, the technique used is a Metropolishastings (Hastings in Biometrika 57:97–109, 1970), and the Gibbs sampler (Casella and George in The Am Stat 46:167–174, 1992; Gelfand and smith in J Am Stat Assoc 85:398–409, 1990; Gilks and Wild in 41:337–348, 1992). The empirical results concerned on the Standard and Poor’s 500 composite Index (S&P), CAC40, Nasdaq, Nikkei and DowJones stock price indexes reveal that the ARSV-t model provides a better performance than the SVOL model on the MSE and the maximum Likelihood function.
Review of Quantitative Finance and Accounting – Springer Journals
Published: Apr 1, 2011
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