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In a Bayesian approach, we compare the volatility forecasting ability of ARCH, GARCH and stochastic volatility(SV) models, using daily Tehran stock market exchange data(TSE). To estimate the parameters of the models, Markov chain Monte Carlo(MCMC) methods is applied. The results show that the SV models perform better than the ARCH and GARCH family.
In this paper, the authors propose a new stochastic model, SVDJ model which has allowed for jump risk, to describe the dynamics behavior of spot exchange rate, and develop a MCMC (Markov chain Monte Carlo) method for the estimation of parameters, jumps, and volatility. Our empirical results indicate the significant existence of jumps in exchange rate process, and the incorporation of jump risk in...
This paper presents a methodology for setting up a decision support system for user interface design (DSUID). We first motivate the role and contributions of DSUID and then demonstrate its implementation in the case of usability diagnosis of Web pages, based on time analysis of clickstream data. The resulting DSUID diagnostic reports enable website managers to learn about possible sources of usability...
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