Financial Returns, Risk and Distributional Properties – International Equity Markets: Concepts, Evidence and Empirical Application

About This Resource Financial Returns, Risk and Distributional Properties is an applied econometrics learning resource designed to develop conceptual understanding and practical competence in the analysis of financial-market returns. It provides a structured progression from the measurement of equity returns to the examination of risk, distributional characteristics and the temporal behaviour of volatility, combining financial and statistical reasoning with empirical application. The resource begins by explaining why financial returns provide a more meaningful basis for analysing equity-market performance than comparisons of price-index levels. It develops the distinction between simple and logarithmic returns and between realised, expected and unexpected returns before introducing the first four moments of a return distribution: mean, variance, skewness and kurtosis. Population and sample measures are distinguished, with emphasis placed on asymmetry, tail behaviour, Pearson and excess kurtosis, and departures from normality through the Jarque–Bera test. The empirical analysis uses monthly MSCI price indices for selected international equity markets from January 1994 to September 2023. The sample includes global and regional benchmarks together with developed and emerging country markets, allowing average returns, volatility, skewness, kurtosis and return-to-volatility ratios to be compared. The analysis distinguishes descriptive historical evidence from expected returns, causal relationships and formal asset-pricing tests. The resource subsequently extends the analysis from unconditional distributional characteristics to the temporal behaviour of volatility. Squared returns, rolling volatility and correlograms are used to examine volatility clustering, time variation in risk and the possibility that return magnitudes remain serially dependent even when returns themselves show little linear autocorrelation. This provides the conceptual and empirical motivation for conditional-variance modelling through ARCH and GARCH processes, while formal estimation of these models is reserved for the subsequent learning resource. EViews is used to implement the empirical procedures, with reproducibility supported by a separate supplementary appendix containing practical guidance and selected diagnostic outputs. The emphasis throughout is not simply on obtaining statistical results, but on understanding what each measure captures, how it should be interpreted and what conclusions the evidence does and does not support. The resource is intended for students, researchers and practitioners seeking a rigorous but accessible bridge between the statistical foundations of financial returns and their practical econometric analysis. This publication is provided for educational purposes as a teaching and learning resource. It does not constitute financial or investment advice.