Model selection problems in conditional volatility.

Summary This doctoral thesis, composed of three chapters, contributes to the development of the problematic on the selection of GARCH-type volatility models. The first chapter proposes a simulation study on model selection in the specific framework of regime-switching models. Simulation experiments are proposed to highlight the inefficiency of the usual selection criteria in particular cases, which can lead to misspecification during model selection. The second chapter proposes a test of the Lagrange multiplier of misspecification in univariate GARCH models. The null hypothesis assumes that the data generating process is a linear GARCH model while under the alternative hypothesis it corresponds to an unknown functional form that is linearized using a Taylor expansion. The test is illustrated in an empirical application on exchange rates. The last chapter studies the impact of oil prices on the sovereign credit default swap spreads of two oil exporting countries: Venezuela and Russia. Using recent data, we find that oil price returns impact Venezuela's sovereign CDS spreads directly while it goes through the exchange rate channel for Russia. This chapter employs advanced statistical methods, including the use of Markov regime-switching models. Finally, the appendix provides a manual for the MSGtool (Matlab) toolbox which provides a collection of functions for studying Markovian regime-switching models. The toolbox is very user-friendly.