Essays on financial econometrics

This dissertation contains three self-contained chapters dealing with volatility modeling and forecasting.; In the first chapter we compare a set of standard GARCH models with a group of Markov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecast the US stock market volatility at horizons that range from one day to one month. The empirical analysis demonstrates that MRS-GARCH models do really outperform all standard GARCH models in forecasting volatility at horizons shorter than one week. In particular, all tests reject the presence of a better model than the MRS-GARCH with normal innovations. However, at forecast horizons longer than one week, standard asymmetric GARCH models tend to be superior.; In chapter 2 a new model to analyze the comovements in the volatilities of a portfolio is proposed. The Pure Variance Common Features model is a factor model for the conditional variances of a portfolio of assets, designed to isolate a small number of variance features that drive all assets' volatilities. It decomposes the conditional variance into a short-run idiosyncratic component (a low-order ARCH process) and a long-run component (the variance factors). An empirical example provides evidence that models with very few variance features perform well in capturing the long-run common volatilities of the equity components of the Dow Jones.; In the third and last chapter we compare standard univariate models and multivariate factor models in terms of their ability to forecast the realized variances of a group of major international stock exchanges. Our results show that those models adopting equally weighted regional factors outperform all the others. In addition, models that use factors obtained from canonical correlation analysis tend to outperform all the others that employ different multivariate techniques, therefore confirming their predicting power.