| Summary: | Despite the traditional separation of academic studies regarding macroeconomics and financial markets, recently, there has been increased interest in investigating the relationship between them based on models of the term structure of interest rates. This thesis in “Macro-finance” connects macroeconomic variables and the fixed income financial markets, both Treasury and corporate. Traditional economic models in linking these markets with the macroeconomy concentrate on the determination of the short rate, as the policy instrument, via the familiar Taylor-rule. The essays in this thesis provide evidence of the mutual relationships in two dimensions: a) price formation in these markets and macroeconomic conditions originating from home and abroad, and b) information originating in these markets and expectations regarding the future state of the economy. In the first essay, we study the impact of oil price shocks in the global crude oil market on the dynamics of the entire term structure. The responses of the yield factors to oil market shocks are shown to differ contingent on the underlying sources driving oil price shocks and the country's dependency on oil. The oil supply and demand shocks explain a considerable amount of variations in the term structure of interest rates, especially in countries with high oil dependency. The second essay tests the predictive power of economic policy uncertainty (EPU) for future bond returns. Using the policy uncertainty measure recently developed by Baker et al. (2016), we investigate the relationship between economic uncertainty and excess bond returns. The impact of the uncertainty is shown to be larger for shorter maturities in near investment horizons. An affine term structure model incorporating the uncertainty factor produces higher fluctuations in term premia estimates which display strong countercyclical movements and accords with expectations. Finally, we examine whether professional forecasters incorporate high-frequency information about credit conditions in revising their economic forecasts. Using Mixed Data Sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both aggregate and individual levels. The relations are shown to be notably strong during ‘bad’ economic conditions, indicating that forecasters anticipate more pronounced effects of credit tightening during economic downturns.
|
|---|
