Upon successful completion of this course, students will be able to: Evaluate and critically reflect upon empirical literature utilizing financial market data. Rigorously apply advanced econometric methodologies to model and analyze asset prices, returns, and volatilities. Map empirical findings back to theoretical frameworks, evaluating how data supports or challenges established financial market theories. Design, execute, and debug computational algorithms

Knowledge and understanding The course provides the advanced theoretical frameworks and empirical toolkits necessary to analyze complex financial data. Students will acquire deep analytical insights to autonomously evaluate the constraints of core financial theories and construct robust, predictive models for key financial variables (e.g., conditional volatility and risk metrics). Applying knowledge and understanding Students will develop the operational skills to: Execute and interpret sophisticated univariate and multivariate time-series models. Implement empirical asset pricing tests and derivative pricing models. Conduct rigorous event studies to quantify the impact of macroeconomic announcements and corporate disclosures on asset prices. Making judgements Students will develop a critical perspective on the empirical boundaries, challenges, and structural assumptions underlying financial models. Through hands-on replication using real-world data, they will learn to distinguish between statistical significance and economic significance, assessing the validity of financial theories under realistic market conditions. Communication and learning skills This course equips students with a self-sufficient workflow spanning economic theory, econometric modeling, and quantitative programming. Graduates will possess the quantitative literacy required to autonomously investigate new financial phenomena and clearly communicate empirical results to both academic and professional audiences.

Foundations of Financial Time Series: Univariate and multivariate linear models. Conditional Heteroskedasticity and Volatility Modeling: Univariate GARCH frameworks (ARCH, GARCH, EGARCH, GJR-GARCH). Multivariate GARCH specifications (DCC, BEKK). The Predictability of Asset Returns: Efficient Market Hypothesis (EMH) tests and technical trading rules. Market Microstructure: Ex-post volatility modeling, realized variance, and high-frequency data properties. Event Study Methodology: Statistical and economic setups for event-window analysis. Empirical Asset Pricing: Theoretical foundations and empirical validation of the CAPM, APT, multi-factor models, and the Consumption-CAPM (CCAPM). Present Value Relations: Present value models, discount-rate variations, and stock price volatility. Empirical Option Pricing: Black-Scholes implementation, implied volatility smiles, and continuous-time adaptations.

Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press. (Classic/Core reference). Linton, O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press. Gourieroux, C., & Jasiak, J. (2001). Financial Econometrics: Problems, Models, and Methods. Princeton University Press. Supplementary lecture notes, code templates, and empirical papers will be distributed via the course platform.

The course relies on interactive lectures focusing on the intersection of economic intuition and rigorous statistical estimation. Student participation is highly encouraged and factored into active learning evaluations. To bridge theory and practice, weekly problem sets require students to write MATLAB scripts to clean, analyze, and model real-world financial data.

The final grade is determined by a comprehensive evaluation of: Take-Home Empirical Assignment: An independent project requiring the replication or extension of an empirical paper. 30% of the final grade Written Examination: Theoretical questions to evaluate the conceptual understanding, model interpretation, and the intuition behind econometric frameworks. 70% of the grade Non-frequentanti (non-attending students) * MATLAB based test + Written Examination (3 hours exam) - 100% of the final grade

None predefined; conditional on excellent performance in the course and a strong interest in quantitative finance.