FINANCIAL ECONOMETRICS

This course belongs to the fundamentals teaching activities of the course called "Economics and Finance". In line with the educational objectives of the course, this activity aims to present the main mathematical and statistical tools necessary for the analysis of economic phenomena; particular attention will be devoted to the use of formal language and methodological rigor. More specifially, the course aims to complete students preparation in Econometrics by being able to deal with advanced econometric models and methods applied to financial data. Moreover, it will give the student an overview of nonlinear tine series modelling for financial data analysis.

Knowledge and understanding skills.
Attendance and active participation in lectures together with the individual study will allow the student to acquire the following knowledge and understanding skills:
- know and use the main mathematical tools necessary to represent complex phenomena on financial markets;
- know the mathematical techniques useful to implement the proposed models.
- know the statistical techniques useful to test the validity of theoretical financial models and relationships on data.

Ability to apply knowledge and understanding.
Through the interaction with the instructors and peers and through the individual study the student acquires the following abilities:
- know how to use quantitative instruments to cope with complex problems on financial markets;
- know how to choose the most appropriate technique in order to approach concretely the problem under analysis.

Judgment skills, communication skills, learning skills.
Regarding the autonomy of judgment, communication skills and learning abilities, through the personal and group study of the concepts seen in class, the student will be able to:
- formulate rational justifications to use a particular approach to tackle the financial problem at hand, while understanding their relative strengths and weaknesses;
- know how to formulate and communicate sophisticated quantitative analysis of financial data through the use of statistical models.

- Essential Prerequisites

Mathematics:
Matrix Algebra
Series and Sequences
Differential Calculus

Statistics and Probability:
Random Variables and Distribution Theory
Conditional and Unconditional Expectation
Multivariate Linear Regression
Point and Interval Estimation
Hypothesis Testing

- Preferable Prerequisites

Statistics and Probability:
ARMA Time Series Models

1. Financial Return Dynamics and Predictability
• Stylised facts of financial returns
• Efficient Market Hypothesis, Random Walk Hypothesis
• Predictability testing: linear and non-linear methods
• Volatility Tests
2. Asset Pricing and Factor Models
• CAPM, APT: theoretical foundations
• Time-series and cross-sectional testing
• Likelihood methods and two-step estimation
• Statistical factor models (PCA, factor extraction)
3. Classical Volatility Modelling
• Introduction to structural models and the Kalman filter
• State Space Volatility Modelling
• Introduction to observation-driven models
• ARCH and GARCH models
• Multivariate volatility models: Multivariate GARCH and DCC.
4. Extracting and Modelling the Term Structure of Interest Rates
• Yield curve estimation models.
• Dynamic factor models of interest rates.
• Dynamic Nelson–Siegel Model.
5. Score-Driven Volatility Modelling
• Introduction to score-driven models
• Score-driven models for returns volatility
• Score-driven models for realised volatility

Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press.
Diebold, F. X., & Rudebusch, G. D. (2013). Yield Curve Modeling and Forecasting: The Dynamic Nelson–Siegel Approach. Princeton University Press.
Harvey, A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter.
Harvey, A.C. (1994), Time Series Models.
Harvey, A. C. (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press.
Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). Wiley.

By way of evaluation, a main examinations covering both the theory and application of the concepts developed in class will be conducted. However, I will also propose an end-of-course project (homework) for the students sitting the first exam session of the year, that examines students' capability in developing a solution to a problem without limiting themselves to the information given in class. As a consequence the course grade will be based on the homework and the final examination for the first exam session in the year, and only on a final examination for all the other exam sessions. The final grade for the first exam session will be determined using the following weights: 30% Homework, 70% final written exam.

scritto

The points from 18 to 20 are allocated for having correctly answered or solved fully or partially from 60% to 69% of the questions in the exam. The points from 21 to 23 are allocated for having correctly answered or solved fully or partially from 70% to 79% of the questions in the exam. The points from 24 to 26 are allocated for having correctly answered or solved fully or partially from 80% to 89% of the questions in the exam. The points from 27 to 30 are allocated for having correctly answered or solved fully or partially from 90% to 100% of the questions in the exam.

Series of lectures on the various topics

Il corso è svolto in collaborazione con il partenariato esteso GRINS - Growing Resilient, INclusive and Sustainable, codice PE0000018, CUP H73C22000930001, avviso pubblico n. 341/2022 del Piano Nazionale di Ripresa e Resilienza (PNRR), Missione 4 - Componente 2 - Investimento 1.3, finanziato dall’Unione europea - NextGenerationEU.
All’interno del corso possono essere proposti incontri con testimoni aziendali aderenti al progetto, incentrati sullo sviluppo di conoscenze pratiche nella materia oggetto di studio, oltre che sui risultati del progetto stesso.
Questo insegnamento tratta argomenti connessi allo Spoke 4 Sustainable Finance - Work Package n. 3.