Detalle

The proposed course equips students with an in-depth understanding of quantitative techniques essential for empirical finance. The lectures combine theoretical foundations with practical applications implemented in R. The course emphasizes identifying the statistical and dynamic characteristics of financial time series and applying appropriate econometric tools to support informed financial decision-making. By examining key stylized facts of financial data—such as volatility clustering, heavy tails, and downside risk—the course develops strong quantitative skills directly relevant to risk management and portfolio evaluation.

Proposed course outline:
Static and dynamic risk measures in financial data
Standard deviation and downside standard deviation
Beta and downside beta
Static Value at Risk and Expected Shortfall
Volatility modeling using the GARCH family
Dynamic Value at Risk and Expected Shortfall
Backtesting and model validation

The application of these methods equips students to quantify, model, and forecast financial risk under both normal and stressed market conditions, enabling critical evaluation of model performance and risk forecasts. As a result, students develop the ability to support robust, data-driven financial decisions that account for tail risk, asymmetry, and time-varying uncertainty—central considerations in modern risk management and empirical asset pricing.