Download or read book Modeling Stock Volatility with Stochastic ARCH, GARCH and Stochastic Volatility Model written by Chang Sun (M.S. in Statistics) and published by . This book was released on 2016 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling volatility within the log stock return is key to the stock price prediction. Despite numerous researches that modeled the volatility with conditional heavy-tailed error distributions, the unconditional distribution remains unknown. In this report, we use and follow the method introduced by Pitt and Walker (2005) by assigning a Student-t distribution for the marginal density of log return and constructing three models respectively, with similar structures to Autoregressive Conditional Heteroskedasticity (ARCH), Generalized ARCH (GARCH) and Stochastic Volatility model in a Bayesian way. We demonstrate the capability of the three models for stock price prediction with S&P 500 index and show that all our models outperform the standard GARCH model (Bollerslev, 1986).