Summary In terms of the specific topics covered in the thesis, my research aims to further understanding of risky asset return and volatility behaviour from a macro-finance perspective. In three of the four chapters, the macro drivers of both risky asset returns (the first moment) and volatility (the second moment) are studied and analyzed in detail across different geographies and various time periods. The use of both long sample sets and relevant sub-sample periods allows for a more in-depth assessment of the nature and form of these drivers as well as their influence on risky asset return and volatility dynamics, whilst weakening the impact of any endogeneity bias which the empirical estimation framework used may be subject to. The earliest data used in this research starts from the 18th century. In the first chapter, entitled “Macro Drivers of Equity Market Volatility”, the focus is on the construction and analysis of macro state variables, which are shown to have a strong influence on the behaviour of equity return volatility, especially during periods of severe market upheaval. Chapter two examines the relative abilities of GARCH and Stochastic Volatility Models (SV) to forecast volatility, in a world where the true model can be depicted by an EGARCH(1,2) formulation. Turning to chapter three, the relationship between equity returns and inflation (specifically, if equities are a hedge against inflation) is explored using long-term historical data for the US, the UK, Germany and Japan. Finally, chapter four analytically tackles the question of how various investors' (institutional and retail) asset allocation decisions are dependent on both the formulation of the wealth maximization function and the differentiated nature of information signals. Specifically, this chapter focusses on how asset allocation behaviour of various categories of investors (facing different objective functions) may lead to “herding”.