"This paper proposes the EGARCH [Exponential Generalized Autoregressive Conditional Heteroskedasticity] model with jumps and heavy-tailed errors, and studies the empirical performance of different models including the stochastic volatility models with leverage, jumps and heavy-tailed errors for daily stock returns. In the framework of a Bayesian inference, the Markov chain Monte Carlo estimation methods for these models are illustrated with a simulation study. The model comparison based on the marginal likelihood estimation is provided with data on the U.S. stock index."--Author's abstract.

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).

Victor Silverio Posadas Hernandez explores three sets of questions: What are the investment laws in the Latin American emerging markets (LAEM) and how do they compare to those of developed countries? How heterogeneous are the implicit trading costs in the LAEM and which factors are responsible for the heterogeneity? How does the predictability of stock returns in the LAEM differ from those documented for developed markets?