Statistical Methods for Measuring Market Risk: Applications in Equity Markets

The importance of measuring market risk lies in assessing the uncertainty in the financial sector, estimating the potential loss, and assisting the investment decision in stock markets. The present review focuses on statistical methods for measuring and forecasting market risk, especially when applied to individual stocks, equity indices and investment portfolios. It includes traditional statistical measures such as standard deviation, historical volatility, beta, alpha, Sharpe ratio and R-squared, as well as risk models such as Value at Risk (VaR), Expected Shortfall (ES) and GARCH. We also consider more sophisticated approaches such as asymmetric volatility models, Extreme Value Theory (EVT) and Filtered Historical Simulation (FHS). The review also addresses practical uses and limitations of these methods, especially their sensitivity to historical data, distributional assumptions and extreme market movements. In general, the combination of complementary statistical approaches may provide a more comprehensive assessment of equity market risk.