Exogenous Variables in Dynamic Conditional Correlation Models for Financial Markets

In this dissertation, I analyze determinants of conditional correlations. Specifically, I propose the generalized DCCX model that facilitates the analysis of the effects of exogenous variables such as macroeconomic announcements or other financial time series on conditional correlations. Furthermore, I show that it is necessary to take into account the effect of exogenous variables on conditional variances and demonstrate that employing a GARCHX model for variances is helpful. I test the model with several datasets. I find that conditional correlations between stocks and bonds increase as risk aversion rises even when controlling for macroeconomic announcements. While most macroeconomic news result in falling conditional correlations, the publication of news concerning future interest rates or inflation figures moves bond and stock prices in the same direction.