A comparative study of two models SV with MCMC algorithm

A comparative study of two models SV with MCMC algorithm 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

A comparative study of two models SV with MCMC algorithm

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Springer US
Copyright © 2011 by Springer Science+Business Media, LLC
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
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